Home
Search results β€œWeb mining categories”
Data Mining Lecture - - Advance Topic | Web mining | Text mining (Eng-Hindi)
 
05:13
Data mining Advance topics - Web mining - Text Mining -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~- Follow us on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy
Views: 66137 Well Academy
Web Mining - Tutorial
 
11:02
Web Mining Web Mining is the use of Data mining techniques to automatically discover and extract information from World Wide Web. There are 3 areas of web Mining Web content Mining. Web usage Mining Web structure Mining. Web content Mining Web content Mining is the process of extracting useful information from content of web document.it may consists of text images,audio,video or structured record such as list & tables. screen scaper,Mozenda,Automation Anywhere,Web content Extractor, Web info extractor are the tools used to extract essential information that one needs. Web Usage Mining Web usage Mining is the process of identifying browsing patterns by analysing the users Navigational behaviour. Techniques for discovery & pattern analysis are two types. They are Pattern Analysis Tool. Pattern Discovery Tool. Data pre processing,Path Analysis,Grouping,filtering,Statistical Analysis, Association Rules,Clustering,Sequential Pattterns,classification are the Analysis done to analyse the patterns. Web structure Mining Web structure Mining is a tool, used to extract patterns from hyperlinks in the web. Web structure Mining is also called link Mining. HITS & PAGE RANK Algorithm are the Popular Web structure Mining Algorithm. By applying Web content mining,web structure Mining & Web usage Mining knowledge is extracted from web data.
Data Mining - Clustering
 
06:52
What is clustering Partitioning a data into subclasses. Grouping similar objects. Partitioning the data based on similarity. Eg:Library. Clustering Types Partitioning Method Hierarchical Method Agglomerative Method Divisive Method Density Based Method Model based Method Constraint based Method These are clustering Methods or types. Clustering Algorithms,Clustering Applications and Examples are also Explained.
How to scrape data from various categories of a website ?
 
06:25
www.webharvy.com In this video we will see how the Category Scraping feature of WebHarvy can be used to extract product details listed under various categories/sub-categories of ecommerce websites. Know more about WebHarvy: http://www.webharvy.com/articles/getting-started.html Know more about category scraping feature: http://www.webharvy.com/tour7.html Various pagination techniques: http://www.webharvy.com/tour3.html Contact our support: http://www.webharvy.com/support.html
Views: 350 sysnucleus
Introduction to Data Mining: Basic Data Types
 
04:29
Continuing our series on Data Mining Fundamentals, we introduce you to the three data set types, Record, Ordered, and Graph and give you examples of when you would want to use each data set. -- Learn more about Data Science Dojo here: https://hubs.ly/H0hCm360 Watch the latest video tutorials here: https://hubs.ly/H0hCmnR0 See what our past attendees are saying here: https://hubs.ly/H0hCmp10 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_scienc... Vimeo: https://vimeo.com/datasciencedojo
Views: 14230 Data Science Dojo
Extracting Product Info from various categories in supermarket.souq.com | Webharvy
 
04:31
www.webharvy.com 2 stage scraping : Stage 1 : collection of main category URLs Stage 2 : Creating a config file for all subcategories and products in each category with 'Scrape a list of similar links' feature
Views: 310 sysnucleus
Automatic Classification of Documents using RapidMiner
 
11:09
This is part 5 of a 5 part video series on Text Mining using the free and open-source RapidMiner. This video describes how to automatically classify documents using the Nearest Neighbor algorithm, and finding out which words are important to classification using the Naive Bayes learner. Cross-Validation is also covered.
Views: 56517 el chief
Categories and Characteristics of Web Systems
 
01:07
Categories and Characteristics of Web Systems
Automatic Text Classification
 
05:56
We trained a Multinomial Naive Bayes algorithm to classify any text into one of the 20 categories . This algorithm is hosted in a web portal and hosted at this url http://52.45.171.205:3500 for those who are interested in trying it our. Please reach out to us at www.datalabs.optisolbusiness.com.
Views: 153 Optisol Data Labs
OLAP Servers ll ROLAP, MOLAP, HOLAP Explained In Hindi
 
05:25
ROLAP MOLAP HOLAP These OLAP SERVERS are explained in this video πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“ SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING
Views: 45877 5 Minutes Engineering
eCommerce Shop in Arabic #056 - Categories - Create Update Page
 
08:20
Create Categories Update Page
Views: 4971 Elzero Web School
text mining, web mining and sentiment analysis
 
13:28
text mining, web mining
Views: 1646 Kakoli Bandyopadhyay
Prepare your data for ML  | Text Classification Tutorial Pt. 1 (Coding TensorFlow)
 
04:25
@lmoroney is back with another episode of Coding TensorFlow! In this episode, we discuss Text Classification, which assigns categories to text documents. This is part 1 of a 2 part sub series that focuses on the data and gets it ready to train a neural network. Laurence also explains the unique challenges associated with Text Classification. Watch to follow along and stay tuned for part 2 of this episode where we’ll look at how to design a neural network to accept the data we prepared. Hands on tutorial β†’ http://bit.ly/2CNVMbi Watch Part 2 https://www.youtube.com/watch?v=vPrSca-YjFg Subscribe to TensorFlow β†’ http://bit.ly/TensorFlow1 Watch more Coding TensorFlow β†’ http://bit.ly/2zoZfvt
Views: 23846 TensorFlow
K mean clustering algorithm with solve example
 
12:13
#kmean datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 466900 Last moment tuitions
Weka Text Classification for First Time & Beginner Users
 
59:21
59-minute beginner-friendly tutorial on text classification in WEKA; all text changes to numbers and categories after 1-2, so 3-5 relate to many other data analysis (not specifically text classification) using WEKA. 5 main sections: 0:00 Introduction (5 minutes) 5:06 TextToDirectoryLoader (3 minutes) 8:12 StringToWordVector (19 minutes) 27:37 AttributeSelect (10 minutes) 37:37 Cost Sensitivity and Class Imbalance (8 minutes) 45:45 Classifiers (14 minutes) 59:07 Conclusion (20 seconds) Some notable sub-sections: - Section 1 - 5:49 TextDirectoryLoader Command (1 minute) - Section 2 - 6:44 ARFF File Syntax (1 minute 30 seconds) 8:10 Vectorizing Documents (2 minutes) 10:15 WordsToKeep setting/Word Presence (1 minute 10 seconds) 11:26 OutputWordCount setting/Word Frequency (25 seconds) 11:51 DoNotOperateOnAPerClassBasis setting (40 seconds) 12:34 IDFTransform and TFTransform settings/TF-IDF score (1 minute 30 seconds) 14:09 NormalizeDocLength setting (1 minute 17 seconds) 15:46 Stemmer setting/Lemmatization (1 minute 10 seconds) 16:56 Stopwords setting/Custom Stopwords File (1 minute 54 seconds) 18:50 Tokenizer setting/NGram Tokenizer/Bigrams/Trigrams/Alphabetical Tokenizer (2 minutes 35 seconds) 21:25 MinTermFreq setting (20 seconds) 21:45 PeriodicPruning setting (40 seconds) 22:25 AttributeNamePrefix setting (16 seconds) 22:42 LowerCaseTokens setting (1 minute 2 seconds) 23:45 AttributeIndices setting (2 minutes 4 seconds) - Section 3 - 28:07 AttributeSelect for reducing dataset to improve classifier performance/InfoGainEval evaluator/Ranker search (7 minutes) - Section 4 - 38:32 CostSensitiveClassifer/Adding cost effectiveness to base classifier (2 minutes 20 seconds) 42:17 Resample filter/Example of undersampling majority class (1 minute 10 seconds) 43:27 SMOTE filter/Example of oversampling the minority class (1 minute) - Section 5 - 45:34 Training vs. Testing Datasets (1 minute 32 seconds) 47:07 Naive Bayes Classifier (1 minute 57 seconds) 49:04 Multinomial Naive Bayes Classifier (10 seconds) 49:33 K Nearest Neighbor Classifier (1 minute 34 seconds) 51:17 J48 (Decision Tree) Classifier (2 minutes 32 seconds) 53:50 Random Forest Classifier (1 minute 39 seconds) 55:55 SMO (Support Vector Machine) Classifier (1 minute 38 seconds) 57:35 Supervised vs Semi-Supervised vs Unsupervised Learning/Clustering (1 minute 20 seconds) Classifiers introduces you to six (but not all) of WEKA's popular classifiers for text mining; 1) Naive Bayes, 2) Multinomial Naive Bayes, 3) K Nearest Neighbor, 4) J48, 5) Random Forest and 6) SMO. Each StringToWordVector setting is shown, e.g. tokenizer, outputWordCounts, normalizeDocLength, TF-IDF, stopwords, stemmer, etc. These are ways of representing documents as document vectors. Automatically converting 2,000 text files (plain text documents) into an ARFF file with TextDirectoryLoader is shown. Additionally shown is AttributeSelect which is a way of improving classifier performance by reducing the dataset. Cost-Sensitive Classifier is shown which is a way of assigning weights to different types of guesses. Resample and SMOTE are shown as ways of undersampling the majority class and oversampling the majority class. Introductory tips are shared throughout, e.g. distinguishing supervised learning (which is most of data mining) from semi-supervised and unsupervised learning, making identically-formatted training and testing datasets, how to easily subset outliers with the Visualize tab and more... ---------- Update March 24, 2014: Some people asked where to download the movie review data. It is named Polarity_Dataset_v2.0 and shared on Bo Pang's Cornell Ph.D. student page http://www.cs.cornell.edu/People/pabo/movie-review-data/ (Bo Pang is now a Senior Research Scientist at Google)
Views: 140414 Brandon Weinberg
Simple Deep Neural Networks for Text Classification
 
14:47
Hi. In this video, we will apply neural networks for text. And let's first remember, what is text? You can think of it as a sequence of characters, words or anything else. And in this video, we will continue to think of text as a sequence of words or tokens. And let's remember how bag of words works. You have every word and forever distinct word that you have in your dataset, you have a feature column. And you actually effectively vectorizing each word with one-hot-encoded vector that is a huge vector of zeros that has only one non-zero value which is in the column corresponding to that particular word. So in this example, we have very, good, and movie, and all of them are vectorized independently. And in this setting, you actually for real world problems, you have like hundreds of thousands of columns. And how do we get to bag of words representation? You can actually see that we can sum up all those values, all those vectors, and we come up with a bag of words vectorization that now corresponds to very, good, movie. And so, it could be good to think about bag of words representation as a sum of sparse one-hot-encoded vectors corresponding to each particular word. Okay, let's move to neural network way. And opposite to the sparse way that we've seen in bag of words, in neural networks, we usually like dense representation. And that means that we can replace each word by a dense vector that is much shorter. It can have 300 values, and now it has any real valued items in those vectors. And an example of such vectors is word2vec embeddings, that are pretrained embeddings that are done in an unsupervised manner. And we will actually dive into details on word2vec in the next two weeks. But, all we have to know right now is that, word2vec vectors have a nice property. Words that have similar context in terms of neighboring words, they tend to have vectors that are collinear, that actually point to roughly the same direction. And that is a very nice property that we will further use. Okay, so, now we can replace each word with a dense vector of 300 real values. What do we do next? How can we come up with a feature descriptor for the whole text? Actually, we can use the same manner as we used for bag of words. We can just dig the sum of those vectors and we have a representation based on word2vec embeddings for the whole text, like very good movie. And, that's some of word2vec vectors actually works in practice. It can give you a great baseline descriptor, a baseline features for your classifier and that can actually work pretty well. Another approach is doing a neural network over these embeddings.
Views: 19885 Machine Learning TV
Text Classification Using RapidMiner
 
02:44
Part 2 of 2. This video discusses the classification of text in RapidMiner. There are three types of classification: - Decision Trees - KNN - Naive Bayes in this video, we used Naive Bayes classification method.
Views: 2863 Alaa Khalid
How to Build a Text Mining, Machine Learning Document Classification System in R!
 
26:02
We show how to build a machine learning document classification system from scratch in less than 30 minutes using R. We use a text mining approach to identify the speaker of unmarked presidential campaign speeches. Applications in brand management, auditing, fraud detection, electronic medical records, and more.
Views: 168277 Timothy DAuria
Cluster Analysis | Categorization
 
07:04
Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters.
Views: 15812 Red Apple Tutorials
Cloud Computing Services Models - IaaS PaaS SaaS Explained
 
06:43
https://ecoursereview.com/cloud-computing-pros-and-cons-for-business-it/ 3 Types of Cloud Computing Services: IaaS PaaS SaaS Explained You Might Wonder – What Is Cloud Computer: #Cloudcomputing is a set of forms that contain certain elements that allows for on-demand, network access, scalability, and shared resources. It's a platform for managing, storing, and processing data online through the internet. Some of the cloud computing features include the following: - On-Demand Services – Available when you need it - Network Access – When using the internet as your medium - Shared Resources – All resources are gathered together and used by multiple customers - Scalability - The ability of a computer system to adapt to increasing demands The Three Delivery Models: Cloud computing provides different services based on three delivery configurations. When they are arranged in a pyramid structure, they are in the order of SaaS, PaaS, and IaaS. The Three Services: #SaaS - Software as a Service This service provides on-demand pay per use of the application software for users and is independent of a platform. You do not have to install software on your computer, unlike a license paid program. Cloud runs a single occurrence of the software, making it available for multiple end-users allowing the service to be cheap. All the computing resources that are responsible for delivering SaaS are totally managed by the vendor. The service is accessible through a web browser or lightweight client applications. End customers use SaaS regularly. The most popular SaaS providers offer the following products and services: Google Ecosystem including Gmail, Google Docs, Google Drive, Microsoft Office 365, and SalesForce. #PaaS - Platform as a Service This service is mostly a development environment that is made up of a programming language execution environment, an operating system, web server, and database. It provides an environment where users can build, compile, and run their program without worrying about an hidden infrastructure. You manage the data and application resources. All the other resources are managed by the vendor. This is the realm for developers. PaaS providers offer the following products and services: Amazon Web services, Elastic Beanstalk, Google App Engine, Windows Azure, Heroku, and Force.com #IaaS - Infrastructure as a Service This service provides the architecture and infrastructure. It provides all computing resources but in a virtual environment so multiple users can have access. The resources include data storage, virtualization, servers, and networking. Most vendors are responsible for managing them. If you use this service, you are responsible for handling other resources including applications, data, runtime, and middleware. This is mostly for SysAdmins. IaaS providers offer the following products and services: Amazon EC2, Go Grid, and Rackspace.com. Here is a short list of some companies that use cloud computing: iCloud – Cloud from Apple is for Apple products. You can backup and store everything from multimedia to documents online. The content is then smoothly integrated onto your devices. Amazon's AWS - When you talk about companies using cloud computing, Amazon Web Services leads the pack. It offers IaaS and PaaS to all their customers. Google Cloud – This cloud platform is universal for Google's enormous ecosystem and for other products such as Microsoft Office. It provides storage of data and collaboration along with other services that are included in their cloud computing suite. Microsoft Azure – Offered by Microsoft, it provides SaaS, PaaS, and IaaS for its software and developer tools. If you have used Office 365, then you have used SaaS. IBM Smart Cloud - This offers private, public, and hybrid distribution platforms providing a full range of SaaS, PaaS, and IaaS cloud computing services for businesses. The pay as you go platform generates profits for IBM. New technology is popping up all over the internet and Cloud seems to be on the rise. This is only scratching the surface on what is already available and what will become available throughout 2017. --- 3 Types of Cloud Computing Services - IaaS PaaS SaaS Explained https://www.youtube.com/watch?v=36zducUX16w #CloudComputingServices #CloudComputing --- Follow us on https://www.facebook.com/EcourseReviews https://twitter.com/EcourseReviews https://plus.google.com/+Ecoursereview/posts https://ecoursereview.com/
Views: 460517 Ecourse Review
What is Web Application (Hindi)
 
03:30
What is Web Application HTML Tutorials : http://goo.gl/O254f9 CSS Tutorials: https://goo.gl/1QNdiB SQL Tutorials: https://goo.gl/U4TcEX Check Out Our Other Playlists: https://www.youtube.com/user/GeekyShow1/playlists SUBSCRIBE to Learn Programming Language ! http://goo.gl/glkZMr Learn more about subject: http://www.geekyshows.com/ ________________________________________________ If you found this video valuable, give it a like. If you know someone who needs to see it, share it. If you have questions ask below in comment section. Add it to a playlist if you want to watch it later. ________________________________________________ T A L K W I T H M E ! Business Email: [email protected] Youtube Channel: https://www.youtube.com/c/geekyshow1 Facebook: https://www.facebook.com/GeekyShow Twitter: https://twitter.com/Geekyshow1 Google Plus: https://plus.google.com/+Geekyshowsgeek Website: http://www.geekyshows.com/ _______________________________________________ Make sure you LIKE, SUBSCRIBE, COMMENT, and REQUEST A VIDEO! :) _______________________________________________
Views: 55959 Geeky Shows
Web Scraping Tutorial using WebHarvy - Multi-level Category Scraping
 
13:34
In this video we discuss how WebHarvy can be configured to automatically traverse categories and sub-categories within a website and scrape details from multiple listing pages using a single configuration. Know more about category scraping https://www.webharvy.com/tour7.html https://www.webharvy.com/index.html Download Free Trial :- https://www.webharvy.com/download.html Have questions ? Contact our tech support :- https://www.webharvy.com/contact.html
Views: 1115 sysnucleus
Data Mining, Classification, Clustering, Association Rules, Regression, Deviation
 
05:01
Complete set of Video Lessons and Notes available only at http://www.studyyaar.com/index.php/module/20-data-warehousing-and-mining Data Mining, Classification, Clustering, Association Rules, Sequential Pattern Discovery, Regression, Deviation http://www.studyyaar.com/index.php/module-video/watch/53-data-mining
Views: 91943 StudyYaar.com
Pocket Data Mining
 
12:14
http://www.springer.com/engineering/computational+intelligence+and+complexity/book/978-3-319-02710-4 Pocket Data Mining PDM is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application. Related publications: Stahl F., Gaber M. M., Bramer M., and Yu P. S, Distributed Hoeffding Trees for Pocket Data Mining, Proceedings of the 2011 International Conference on High Performance Computing & Simulation (HPCS 2011), Special Session on High Performance Parallel and Distributed Data Mining (HPPD-DM 2011), July 4 -- 8, 2011, Istanbul, Turkey, IEEE press. http://eprints.port.ac.uk//3523 Stahl F., Gaber M. M., Bramer M., Liu H., and Yu P. S., Distributed Classification for Pocket Data Mining, Proceedings of the 19th International Symposium on Methodologies for Intelligent Systems (ISMIS 2011), Warsaw, Poland, 28-30 June, 2011, Lecture Notes in Artificial Intelligence LNAI, Springer Verlag. http://eprints.port.ac.uk/3524/ Stahl F., Gaber M. M., Bramer M., and Yu P. S., Pocket Data Mining: Towards Collaborative Data Mining in Mobile Computing Environments, Proceedings of the IEEE 22nd International Conference on Tools with Artificial Intelligence (ICTAI 2010), Arras, France, 27-29 October, 2010. http://eprints.port.ac.uk/3248/
Views: 3023 Mohamed Medhat Gaber
How do I click a list of links or categories?
 
02:00
In this video, a support staff member demonstrates how to click on a list of items via two different methods: 1. Creating a Capture List that clicks on each item in the list. 2. Creating a dedicated Click List.
Views: 939 MozendaSupport
What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning
 
05:30
What is SOCIAL MEDIA MINING? What does SOCIAL MEDIA MINING mean? SOCIAL MEDIA MINING meaning - SOCIAL MEDIA MINING definition - SOCIAL MEDIA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Social media mining is the process of representing, analyzing, and extracting actionable patterns and trends from raw social media data. The term "mining" is an analogy to the resource extraction process of mining for rare minerals. Resource extraction mining requires mining companies to sift through vast quanitites of raw ore to find the precious minerals; likewise, social media "mining" requires human data analysts and automated software programs to sift through massive amounts of raw social media data (e.g., on social media usage, online behaviours, sharing of content, connections between individuals, online buying behaviour, etc.) in order to discern patterns and trends. These patterns and trends are of interest to companies, governments and not-for-profit organizations, as these organizations can use these patterns and trends to design their strategies or introduce new programs (or, for companies, new products, processes and services). Social media mining uses a range of basic concepts from computer science, data mining, machine learning and statistics. Social media miners develop algorithms suitable for investigating massive files of social media data. Social media mining is based on theories and methodologies from social network analysis, network science, sociology, ethnography, optimization and mathematics. It encompasses the tools to formally represent, measure, model, and mine meaningful patterns from large-scale social media data. In the 2010s, major corporations, as well as governments and not-for-profit organizations engage in social media mining to find out more about key populations of interest, which, depending on the organization carrying out the "mining", may be customers, clients, or citizens. As defined by Kaplan and Haenlein, social media is the "group of internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content." There are many categories of social media including, but not limited to, social networking (Facebook or LinkedIn), microblogging (Twitter), photo sharing (Flickr, Photobucket, or Picasa), news aggregation (Google reader, StumbleUpon, or Feedburner), video sharing (YouTube, MetaCafe), livecasting (Ustream or Twitch.tv), virtual worlds (Kaneva), social gaming (World of Warcraft), social search (Google, Bing, or Ask.com), and instant messaging (Google Talk, Skype, or Yahoo! messenger). The first social media website was introduced by GeoCities in 1994. It enabled users to create their own homepages without having a sophisticated knowledge of HTML coding. The first social networking site, SixDegree.com, was introduced in 1997. Since then, many other social media sites have been introduced, each providing service to millions of people. These individuals form a virtual world in which individuals (social atoms), entities (content, sites, etc.) and interactions (between individuals, between entities, between individuals and entities) coexist. Social norms and human behavior govern this virtual world. By understanding these social norms and models of human behavior and combining them with the observations and measurements of this virtual world, one can systematically analyze and mine social media. Social media mining is the process of representing, analyzing, and extracting meaningful patterns from data in social media, resulting from social interactions. It is an interdisciplinary field encompassing techniques from computer science, data mining, machine learning, social network analysis, network science, sociology, ethnography, statistics, optimization, and mathematics. Social media mining faces grand challenges such as the big data paradox, obtaining sufficient samples, the noise removal fallacy, and evaluation dilemma. Social media mining represents the virtual world of social media in a computable way, measures it, and designs models that can help us understand its interactions. In addition, social media mining provides necessary tools to mine this world for interesting patterns, analyze information diffusion, study influence and homophily, provide effective recommendations, and analyze novel social behavior in social media.
Views: 1687 The Audiopedia
Semantria Web Configurator - Categories
 
01:30
This tutorial video will show you how to adjust your Category settings for Semantria Web Configurator.
Views: 454 Lexalytics
ParseHub Tutorial: Clicking into Product Categories and Subcategories
 
06:35
This video tutorial demonstrates how you can click into categories and their respective subcategories on an e-commerce website using ParseHub. The following tutorials contain more information on scraping e-commerce websites: Scrape product details: https://www.youtube.com/watch?v=HJI1qzn1Od4 Scrape product categories (written tutorial): https://help.parsehub.com/hc/en-us/articles/219063028-Scrape-product-categories Scrape product details (written tutorial): https://help.parsehub.com/hc/en-us/articles/221197507-Scrape-product-details Find out more about ParseHub at https://www.parsehub.com/ and visit our Help Centre at https://help.parsehub.com for more tutorials. Contact us at [email protected] should you have any questions or issues with your own project.
Views: 1784 ParseHub
Rat Hole Mining in Meghalaya - Thin Coal Seams (Important - Current Affairs)
 
04:31
Dr. Manishika Jain discusses the issue of rat hole mining in Meghalaya and ban of this mining - cause and consequences Refer - https://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Geography-Series.htm Also refer https://www.doorsteptutor.com/Exams/IAS/Mains/Optional/Geography/ #IAS #UPSC #rathole ngt ban on rat hole mining in meghalaya rat hole mining in cherrapunji disadvantages of rat hole mining rat hole mining upsc history of coal mining in meghalaya rat hole mining diagram rat hole mining in meghalaya upsc rat hole mining the hindu Mining of Coal @0:19 Two Types of Coal Structure @0:24 Coal of Seams @0:26 Thin Coal Seams @0:53 Meghalaya Economic @1:06 Rat Hole Mining @1:23 Government of Meghalaya @2:45 Seams @3:50 #Historical #Globally #Production #Environment #Structure #Mining #Seams #Coal #Thin #Economic #Manishika #Examrace
Views: 5919 Examrace
Find the BEST Selling Categories on Ebay with Python
 
11:34
doing a little research to figure out which categories have the best sales on ebay right now
Views: 2392 ifTrue
How to start mining X-CASH (XCASH) on pool with AMD GPU's
 
16:02
Thank You For Watching! Remember to subscribe and hit the bell "πŸ””" icon to get notifications as soon as I upload a new video or start a live stream! How to start mining X-CASH (XCASH) on pool with AMD GPU's. Explained in details how to mine X-CASH (XCASH) on pool with AMD GPU's. 00:31 - Useful Links 01:24 - Mining Pools 01:57 - Wallet 06:21 - Mining Software 07:26 - GPU Drivers 08:08 - Mining Example Useful Links: Official Website: https://www.x-cash.org/ X-CASH (XCASH) Block Explorer: https://explorer.x-cash.org/Explorer BitcoinTalk Thread: https://bitcointalk.org/index.php?topic=4781246.0 X-CASH (XCASH) Exchanges: https://cryptopia.co.nz/ https://tradesatoshi.com/ X-CASH (XCASH) Mining Pools: https://xcash.steadyhash.org/ http://minexcash.com/ https://xcash.luckypool.io/ https://xcash.hashvault.pro/ http://xca.hashparty.net/ http://xca.rubypool.com/ https://xcashpool.xyz/ https://xcash.herominers.com/ https://xcash.arhash.xyz/ MINING Software for mining X-CASH (XCASH) with AMD GPU's: SRBMiner Cryptonight AMD GPU Miner: https://bitcointalk.org/index.php?topic=3167363 http://www.srbminer.com/ dev fee: 0.85% JCE Cryptonote GPU Miner: https://bitcointalk.org/index.php?topic=3281187.0 dev fee: 2.1% Cast XMR - Highspeed CryptoNight Miner: https://bitcointalk.org/index.php?topic=2256917.0 http://www.gandalph3000.com/ dev fee: 1% TeamRedMiner CNv8: https://bitcointalk.org/index.php?topic=5059817.0 dev fee: 2.5% Link to official wallets: https://www.x-cash.org/downloads/ https://github.com/X-CASH-official/X-CASH/releases/ Web Wallet: https://wallet.x-cash.org/ Drivers for GPU's: RX 4XX, RX 5XX, VEGA series: Radeon Software Crimson ReLive Edition Beta for Blockchain Compute Release Notes https://support.amd.com/en-us/kb-articles/Pages/Radeon-Software-Crimson-ReLive-Edition-Beta-for-Blockchain-Compute-Release-Notes.aspx AMD/ATI Pixel Clock Patcher: https://www.monitortests.com/forum/Thread-AMD-ATI-Pixel-Clock-Patcher or you can try latest one from AMD. http://www.guru3d.com/files-categories/videocards-ati-catalyst-vista-win-7.html https://support.amd.com/en-us/download After installing drivers install MSI afterburner or Sapphire TRIXXX or OverdriveNTool - tool for AMD Hawaii, Fiji, Polaris, Vega GPUs, software to be able to proper setup your clocks for your cardΒ’s. You can download this from here: https://www.msi.com/page/afterburner http://www.sapphiretech.com/catapage_tech.asp?cataid=291&lang=eng https://forums.guru3d.com/threads/overdriventool-tool-for-amd-gpus.416116/ For mining X-CASH (XCASH) with SRBMiner you need to edit this 2 files: pools.txt and start.bat In start.bat you need to change default mining algorithm SRBMiner-CN.exe --config Config\config-normalv8.txt --pools XCASH.txt --logfile %LOGTIME% Pools.txt { "pools" : [ {"pool" : "xcash.steadyhash.org:3755", "wallet" : "XCA1azGfPAFih3mCoQ6Y6bZPy1uXeTc7kRpdMh1CF3yXWNMQvac7anndhXao5WFmkVR9gGGqiNxUnTya35m1GTLP57U2grjCM7.80000", "password" : "RIG006", "cryptonight_type" : "normalv8"}, {"pool" : "xcash.steadyhash.org:3755", "wallet" : "XCA1azGfPAFih3mCoQ6Y6bZPy1uXeTc7kRpdMh1CF3yXWNMQvac7anndhXao5WFmkVR9gGGqiNxUnTya35m1GTLP57U2grjCM7.80000", "password" : "RIG006", "cryptonight_type" : "normalv8"} ] } (change worker and address with your own) Crypto Donations Are Really Appreciated! ----------------------------------------------------------------------------- Donate Ethereum and Ethereum-Based (ERC-20) Tokens: 0xdA7d91816d64F6a1682E8540aA96AeE0A0622844 Donate Bitcoin: 19hD3u8XJndTKqGiHmWsNsNvAyCU5gjDmw Donate ETC: 0x0b341f09401d1Aa5D3BC3E10c332AbD05150d469 Special Thanks For The Awesome Music: =================================================== Track: Asketa & Natan Chaim - Alone (feat. Kyle Reynolds) [NCS Release] Music provided by NoCopyrightSounds. Watch: https://youtu.be/q-ooKjw43w8 Free Download / Stream: http://ncs.io/-AloneYO mining expanse mining expanse solo mining ethereum mining ethereum classic decred dual mining eth mining etc mining zcash mining zcash bitcoin btc X-CASH XCASH X-CASH Mining sgminer ccminer monero xmr minergate cryptonight coin magi magi xmg SIA siacoin siamining zclassic zcl zcl mining bip bipcoin bipcoin mining pasc pascal pascalcoin pascalcoin mining dcr decred decred mining lbry lbc dash GEO geocoin qubit veltor vlt thors riddle ethereum classic etc pascal lite pasl chaincoin chc Wyvern WYV Musicoin musicoin music cryptocurrency ubiq ubq sia siacoin sc digibyte dgb groestl zencash zen zencash mining tribus denarius denarius coin mining denarius sigt signatum signatum mining monero xmr pxc phoenixcoin neoscrypt xvg verge myr-gr neoscrypt gobyte gbx hsr rvn x16r x16s masari msr Cryptonight Cryptonight Lite Cryptonight V7 Cryptonight Lite V7 Cryptonight Heavy Cryptonight Haven Cryptonight Fast Cryptonight BitTube V2 Cryptonight StelliteV4 Cryptonight ArtoCash Cryptonight Alloy Cryptonight Italo haven xhv
Views: 1090 Kire Palceski
Top 5 Best Cryptocurrency Wallets
 
12:28
Top 5 Best Cryptocurrency Wallets You'll receive $10 in free bitcoin by signing up with this link http://bit.ly/2oesV41 Bitcoin & Etherum Storage Wallet: http://bit.ly/2o7BmgW Blockchain Training: http://bit.ly/2nGhdn0 What are the different types of Cryptocurrency wallets? There are several types of wallets that provide different ways to store and access your digital currency. Wallets can be broken down into three distinct categories – software, hardware, and paper. Software wallets can be a desktop, mobile or online. Desktop: wallets are downloaded and installed on a PC or laptop. They are only accessible from the single computer in which they are downloaded. Desktop wallets offer one of the highest levels of security however if your computer is hacked or gets a virus there is the possibility that you may lose all your funds. Online: wallets run on the cloud and are accessible from any computing device in any location. While they are more convenient to access, online wallets store your private keys online and are controlled by a third party which makes them more vulnerable to hacking attacks and theft. Mobile: wallets run on an app on your phone and are useful because they can be used anywhere including retail stores. Mobile wallets are usually much smaller and simpler than desktop wallets because of the limited space available on a mobile. Hardware: wallets differ from software wallets in that they store a user’s private keys on a hardware device like a USB. Although hardware wallets make transactions online, they are stored offline which delivers increased security. Hardware wallets can be compatible with several web interfaces and can support different currencies; it just depends on which one you decide to use. What’s more, making a transaction is easy. Users simply plug in their device to any internet enabled computer or device, enter a pin, send currency and confirm. Hardware wallets make it possible to easily transact while also keeping your money offline and away from danger. Paper: wallets are easy to use and provide a very high level of security. While the term paper wallet can simply refer to a physical copy or printout of your public and private keys, it can also refer to a piece of software that is used to securely generate a pair of keys which are then printed. Using a paper wallet is relatively straightforward. Transferring Bitcoin or any other currency to your paper wallet is accomplished by the transfer of funds from your software wallet to the public address shown on your paper wallet. Alternatively, if you want to withdraw or spend currency, all you need to do is transfer funds from your paper wallet to your software wallet. This process, often referred to as β€˜sweeping,’ can either be done manually by entering your private keys or by scanning the QR code on the paper wallet. My name is Ameer Rosic, and I'm a serial entrepreneur, investor, marketing Strategist and Blockchain Evangelist Blog http://www.Ameerrosic.com Blockgeeks: http://www.blockgeeks.com Facebook http://www.Facebook.com/ameerrosic Twitter http://www.Twitter.com/ameerrosic InstaGram http://www.Instagram.com/ameerrosic
Views: 1101935 Ameer Rosic
Basics Of Digital Signature Explained in Hindi
 
08:01
πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š
Views: 24625 5 Minutes Engineering
Security Mechanisms ll Information and Cyber Security Course Explained in Hindi
 
10:36
πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“πŸŽ“ SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘πŸ’‘ THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™πŸ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING πŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“šπŸ“š
Views: 11189 5 Minutes Engineering
What is E-Commerce in Hindi  (Basic Information for Beginners)
 
03:14
Types of E-Commerce : https://youtu.be/m7x6zYEBYEM What is EDI in eCommerce ?: https://youtu.be/zN237-EpFQI ------------------------------------------------------------------------- What is E-Commerce in Hindi what is ecommerce meaning in hindi ecommerce explained e commerce means in hindi ecommerce means introduction to ecommerce in hindi ecommerce theory --------------------------------------------------------------
Views: 200902 STUDY Genius
Introduction to Data Mining: Data Quality
 
02:00
In this Data Mining Fundamentals, we introduce the most overlooked step in data mining, Data Quality. Understanding your data quality problems is very important to creating robust models that will actually work in production. -- Learn more about Data Science Dojo here: https://hubs.ly/H0hCm900 Watch the latest video tutorials here: https://hubs.ly/H0hCm960 See what our past attendees are saying here: https://hubs.ly/H0hCnbN0 -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 4000+ employees from over 830 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo
Views: 7485 Data Science Dojo
IBM Watson Analytics With Faceted Search for IBM Web Content Manager (WCM) Categories
 
35:19
Leverage Watson Analytics to spot trends and drive your business success Businesses need the ability to easily recognize patterns and gain understanding from large amounts of unstructured data quickly and efficiently. Gain the benefits of advanced analytics without the complexity. A smart data discovery service available on the cloud, IBM Watson Analytics enables guided data exploration, automates predictive analytics and facilitates seamless dashboard and infographic creation. This makes it possible for you to interact with data to gain insights into trends and get the answers you need to differentiate in a crowded marketplace and take your business to the next level. Royal Cyber provides consulting and support for IBM Watson Analytics to help you discover valuable nuggets of information buried in your data. In this Webcast you can learn: β€’ How Watson reveals insights β€’ How to make use of Watson’s cognitive computing tools β€’ How Watson can deliver business value Watch now to drive business success and gain an extraordinary advantage over the competition. Contact us today at: [email protected] or Call us at: +1.630.355.6292
Views: 567 Royal Cyber Inc
How to start mining Zcoin (XZC) on pool with nVidia GPU's - New Video 09 2018
 
13:22
Thank You For Watching! Remember to subscribe and hit the bell "πŸ””" icon to get notifications as soon as I upload a new video or start a live stream! How to start mining Zcoin (XZC) on pool with nVidia GPU's. Explained in details how to mine Zcoin (XZC) on pool with NVIDIA GPU's. Useful Links: Official Website: https://zcoin.io/ Bitcointalk Forum: https://bitcointalk.org/index.php?topic=1638450.0 Block Explorer: https://insight.zcoin.io/ https://explorer.zcoin.io/ Zcoin (XZC) Mining Pools: https://www.f2pool.com/ https://zcoin.miningpoolhub.com/ https://mintpond.com/#!/zcoin https://bsod.pw/ http://zergpool.com/ Zcoin (XZC) Exchanges: https://www.binance.com/en https://fexpro.io/ https://upbit.com/home https://bittrex.com/ https://sistemkoin.com/#/ https://crypto-bridge.org/ https://cryptopia.co.nz/ MINING Software for mining Zcoin (XZC) with NVIDIA GPU's: CCminer - opensource - GPL (tpruvot) https://bitcointalk.org/index.php?topic=770064.0 CryptoDredge NVIDIA GPU Miner https://bitcointalk.org/index.php?topic=4807821.0 T-Rex Nvidia GPU miner https://bitcointalk.org/index.php?topic=4432704.0 Link to official wallets: https://github.com/zcoinofficial/zcoin/releases Drivers for GPU's: Latest Nvidia drivers: http://www.nvidia.com/Download/index.aspx http://www.guru3d.com/files-categories/videocards-nvidia-geforce-vista-%7C-7.html MSI afterburner https://www.msi.com/page/afterburner Sapphire TRIXXX http://www.sapphiretech.com/catapage_tech.asp?cataid=291&lang=eng For mining Zcoin (XZC) with CryptoDredge NVIDIA GPU Miner my batch file is like this: @echo off title Zcoin (XZC) - F2Pool CryptoDredge -a lyra2z -o stratum+tcp://xzc.f2pool.com:5740 -u aHjoxgNDAtRGCR7FTo6NeuxQDAtwZkG3N3.RIG001 -p x pause (change worker and address with your own) Crypto Donations Are Really Appreciated! ----------------------------------------------------------------------------- Donate Ethereum and Ethereum-Based (ERC-20) Tokens: 0xdA7d91816d64F6a1682E8540aA96AeE0A0622844 Donate Bitcoin: 19hD3u8XJndTKqGiHmWsNsNvAyCU5gjDmw Donate ETC: 0x0b341f09401d1Aa5D3BC3E10c332AbD05150d469 Special Thanks For The Awesome Music: ================================================================ Song: Culture Code - Make Me Move (feat. Karra) [Tobu Remix] | NCS Release Music provided by NoCopyrightSounds. Watch: https://youtu.be/MRwmxS1AL6E Download/Stream: http://ncs.io/TobuRemixCr mining expanse mining expanse solo mining ethereum mining ethereum classic decred dual mining eth mining etc mining zcash mining zcash bitcoin btc sgminer ccminer monero xmr minergate cryptonight coin magi magi xmg SIA siacoin siamining zclassic zcl zcl mining dcr decred decred mining lbry lbc lbry credits expanse exp boolberry bbr wild keccak dash veltor vlt thors riddle ethereum classic etc Musicoin musicoin music cryptocurrency ubiq ubq sia siacoin sc digibyte dgb groestl zencash zen zencash mining sigt signatum signatum mining monero xmr pxc phoenixcoin neoscrypt xvg verge myr-gr neoscrypt gobyte gbx hsr hsrminer purk purkmining wildkeccak wildminer ravencoin rvn x16r x16s Straks STAK Zcoin XZC -~-~~-~~~-~~-~- Please watch: "How to start mining ANONymous (ANON) on pool with nVidia GPU's " https://www.youtube.com/watch?v=iXfNoIcoHFU -~-~~-~~~-~~-~-
Views: 1379 Kire Palceski
KEEL Data mining tool demo
 
34:02
KEEL Data minig tool Demo of installation and Working
Views: 4303 Manukumar K J
How to Clean Up Raw Data in Excel
 
10:54
Al Chen (https://twitter.com/bigal123) is an Excel aficionado. Watch as he shows you how to clean up raw data for processing in Excel. This is also a great resource for data visualization projects. Find the rest of the class here: https://skl.sh/31z4p1I Subscribe to Skillshare’s Youtube Channel: http://skl.sh/yt-subscribe Check out all of Skillshare’s classes: http://skl.sh/youtube Like Skillshare on Facebook: https://www.facebook.com/skillshare Follow Skillshare on Twitter: https://twitter.com/skillshare Follow Skillshare on Instagram: http://instagram.com/Skillshare
Views: 106137 Skillshare
Final Year Projects | Web usage mining to improve the design of an e-commerce website
 
09:05
Including Packages ======================= * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 365 Clickmyproject
Social Network Analysis with R | Examples
 
26:25
Social network analysis with several simple examples in R. R file: https://goo.gl/CKUuNt Data file: https://goo.gl/Ygt1rg Includes, - Social network examples - Network measures - Read data file - Create network - Histogram of node degree - Network diagram - Highlighting degrees & different layouts - Hub and authorities - Community detection R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 25257 Bharatendra Rai
Overview of Scalability, Horizontal Scaling, and Vertical Scaling
 
05:02
http://zerotoprotraining.com This video provides an overview of Scalability, Horizontal Scaling, and Vertical Scaling Category: Hardware Tags Scalability, Horizontal Scaling, Vertical Scaling, Overview
Views: 22548 HandsonERP
Ashutosh Jadhav: Knowledge-driven Search Intent Mining
 
01:23:21
http://www.knoesis.org/aboutus/thesis_defense#jadhav ABSTRACT: Understanding users’ latent intents behind search queries is essential for satisfying a user’s search needs. Search intent mining can help search engines to enhance its ranking of search results, enabling new search features like instant answers, personalization, search result diversification, and the recommendation of more relevant ads. Consequently, there has been increasing attention on studying how to effectively mine search intents by analyzing search engine query logs. While state-of-the-art techniques can identify the domain of the queries (e.g. sports, movies, health), identifying domain-specific intent is still an open problem. Among all the topics available on the Internet, health is one of the most important in terms of impact on the user and it is one of the most frequently searched areas. This dissertation presents a knowledge-driven approach for domain-specific search intent mining with a focus on health-related search queries. First, we identified 14 consumer-oriented health search intent classes based on inputs from focus group studies and based on analyses of popular health websites, literature surveys, and an empirical study of search queries. We defined the problem of classifying millions of health search queries into zero or more intent classes as a multi-label classification problem. Popular machine learning approaches for multi-label classification tasks (namely, problem transformation and algorithm adaptation methods) were not feasible due to the limitation of label data creations and health domain constraints. Another challenge in solving the search intent identification problem was mapping terms used by laymen to medical terms. To address these challenges, we developed a semantics-driven, rule-based search intent mining approach leveraging rich background knowledge encoded in Unified Medical Language System (UMLS) and a crowd sourced encyclopedia (Wikipedia). The approach can identify search intent in a disease-agnostic manner and has been evaluated on three major diseases. While users often turn to search engines to learn about health conditions, a surprising amount of health information is also shared and consumed via social media, such as public social platforms like Twitter. Although Twitter is an excellent information source, the identification of informative tweets from the deluge of tweets is the major challenge. We used a hybrid approach consisting of supervised machine learning, rule-based classifiers, and biomedical domain knowledge to facilitate the retrieval of relevant and reliable health information shared on Twitter in real time. Furthermore, we extended our search intent mining algorithm to classify health-related tweets into health categories. Finally, we performed a large-scale study to compare health search intents and features that contribute in the expression of search intent from more than 100 million search queries from smarts devices (smartphones or tablets) and personal computers (desktops or laptops). SLIDES: http://www.slideshare.net/knoesis/ashutosh-thesis
Views: 204 Knoesis Center
Mining your blog search box for TARGETED content marketing ideas
 
03:16
Get the latest content marketing strategies and tactics here: http://www.ryanhanley.com/content-warfare-newsletter/ Many bloggers forget to add a search box feature to their blog, choosing instead to use categories or a related post plugin (if on Wordpress). But the blog search box holds the golden content marketing nuggets targeted to exactly what your website visitors want.
Views: 135 Ryan Hanley