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What is Data Mining?
 
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NJIT School of Management professor Stephan P Kudyba describes what data mining is and how it is being used in the business world.
Views: 407019 YouTube NJIT
How data mining works
 
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In this video we describe data mining, in the context of knowledge discovery in databases. More videos on classification algorithms can be found at https://www.youtube.com/playlist?list=PLXMKI02h3_qjYoX-f8uKrcGqYmaqdAtq5 Please subscribe to my channel, and share this video with your peers!
Views: 223747 Thales Sehn Körting
Introduction to data mining and architecture  in hindi
 
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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://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [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: 199979 Last moment tuitions
DATA MINING EXPLAINED IN HINDI | "ITNA SARA DATA??"
 
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नमस्कार दोस्तों,आज की वीडियो में में आप सभी को DATA MINING के बारे में बताने जा रहा हूँ की आखिर DATA MINING क्या होती है और क्या ये हमारे किसी काम आती हैं या नहीं और आखिर हमारे ज़िन्दगी में इसकी कितनी जरुरत है। आशा करता हूँ आपको ये वीडियो पसंद आएगी अगर आपको वीडियो पसंद आये तो वीडियो को LIKE SHARE और चैनल को SUBSCRIBE जरूर से करे। धन्यवाद। जय हिन्द वन्दे मातरम subscribe our channel on youtube: https://www.youtube.com/channel/UCR_kAPwG59SxWRaUfzk3qoQ facebook: https://www.facebook.com/dropouttechnical/ twitter: https://twitter.com/dropoutechnical google+: https://plus.google.com/u/0/103031877017890269380 -~-~~-~~~-~~-~- Please watch: "MOTO X4 my opinion |"Best phone??"|"worth buy at 21000"🔥" https://www.youtube.com/watch?v=r54C6_667uU -~-~~-~~~-~~-~-
Views: 11443 Dropout Technical
K mean clustering algorithm with solve example
 
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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://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [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: 334094 Last moment tuitions
What is DATA MINING? What does DATA MINING mean? DATA MINING meaning, definition & explanation
 
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What is DATA MINING? What does DATA MINING mean? DATA MINING meaning - DATA MINING definition - DATA MINING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Data mining is an interdisciplinary subfield of computer science. It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself. It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate. The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps. The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.
Views: 7135 The Audiopedia
INTRODUCTION TO DATA MINING IN HINDI
 
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Buy Software engineering books(affiliate): Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2whY4Ke Software Engineering: A Practitioner's Approach by McGraw Hill Education https://amzn.to/2wfEONg Software Engineering: A Practitioner's Approach (India) by McGraw-Hill Higher Education https://amzn.to/2PHiLqY Software Engineering by Pearson Education https://amzn.to/2wi2v7T Software Engineering: Principles and Practices by Oxford https://amzn.to/2PHiUL2 ------------------------------- find relevant notes at-https://viden.io/
Views: 108997 LearnEveryone
INTRODUCTION TO DATA MINING
 
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INTRODUCTION TO DATA MINING
Views: 16768 LearnEveryone
Data mining - definition
 
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Data mining involves analysing databases for patterns and trends in large data sets. The overall goal of the data mining process is to extract knowledge from an existing data set and transform it into a human-understandable structure for further use. Created at http://www.b2bwhiteboard.com
Views: 8611 B2Bwhiteboard
Association analysis: Frequent Patterns, Support, Confidence and Association Rules
 
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This lecture provides the introductory concepts of Frequent pattern mining in transnational databases.
Views: 48387 StudyKorner
Data Mining using R | Data Mining Tutorial for Beginners | R Tutorial for Beginners | Edureka
 
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( R Training : https://www.edureka.co/r-for-analytics ) This Edureka R tutorial on "Data Mining using R" will help you understand the core concepts of Data Mining comprehensively. This tutorial will also comprise of a case study using R, where you'll apply data mining operations on a real life data-set and extract information from it. Following are the topics which will be covered in the session: 1. Why Data Mining? 2. What is Data Mining 3. Knowledge Discovery in Database 4. Data Mining Tasks 5. Programming Languages for Data Mining 6. Case study using R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #LogisticRegression #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). Website: https://www.edureka.co/data-science Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. " Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 64644 edureka!
What is Bitcoin Mining?
 
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For more information: https://www.bitcoinmining.com and https://www.weusecoins.com What is Bitcoin Mining? Have you ever wondered how Bitcoin is generated? This short video is an animated introduction to Bitcoin Mining. Credits: Voice - Chris Rice (www.ricevoice.com) Motion Graphics - Fabian Rühle (www.fabianruehle.de) Music/Sound Design - Christian Barth (www.akkord-arbeiter.de) Andrew Mottl (www.andrewmottl.com)
Views: 6740539 BitcoinMiningCom
Introduction to Datawarehouse in hindi | Data warehouse and data mining Lectures
 
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#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://goo.gl/to1yMH 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: 271011 Last moment tuitions
Python Data Mining
 
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Views: 22080 Alex Stoykov
Introduction to Data Mining: Euclidean Distance & Cosine Similarity
 
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In this Data Mining Fundamentals tutorial, we continue our introduction to similarity and dissimilarity by discussing euclidean distance and cosine similarity. We will show you how to calculate the euclidean distance and construct a distance matrix. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8M8m0 See what our past attendees are saying here: https://hubs.ly/H0f8Lts0 -- 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: 20117 Data Science Dojo
What is DATA MART? What does DATA MART mean? DATA MART meaning, definition & explanation
 
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✪✪✪✪✪ WORK FROM HOME! Looking for WORKERS for simple Internet data entry JOBS. $15-20 per hour. SIGN UP here - http://jobs.theaudiopedia.com ✪✪✪✪✪ ✪✪✪✪✪ The Audiopedia Android application, INSTALL NOW - https://play.google.com/store/apps/details?id=com.wTheAudiopedia_8069473 ✪✪✪✪✪ What is DATA MART? What does DATA MART mean? DATA MART meaning - DATA MART definition - DATA MART 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 A data mart is the access layer of the data warehouse environment that is used to get data out to the users. The data mart is a subset of the data warehouse and is usually oriented to a specific business line or team. Whereas data warehouses have an enterprise-wide depth, the information in data marts pertains to a single department. In some deployments, each department or business unit is considered the owner of its data mart including all the hardware, software and data. This enables each department to isolate the use, manipulation and development of their data. In other deployments where conformed dimensions are used, this business unit ownership will not hold true for shared dimensions like customer, product, etc. Organizations build data warehouses and data marts because the information in the database is not organized in a way that makes it readily accessible, requiring queries that are too complicated or resource-consuming. While transactional databases are designed to be updated, data warehouses or marts are read only. Data warehouses are designed to access large groups of related records. Data marts improve end-user response time by allowing users to have access to the specific type of data they need to view most often by providing the data in a way that supports the collective view of a group of users. A data mart is basically a condensed and more focused version of a data warehouse that reflects the regulations and process specifications of each business unit within an organization. Each data mart is dedicated to a specific business function or region. This subset of data may span across many or all of an enterprise’s functional subject areas. It is common for multiple data marts to be used in order to serve the needs of each individual business unit (different data marts can be used to obtain specific information for various enterprise departments, such as accounting, marketing, sales, etc.). The related term spreadmart is a derogatory label describing the situation that occurs when one or more business analysts develop a system of linked spreadsheets to perform a business analysis, then grow it to a size and degree of complexity that makes it nearly impossible to maintain.
Views: 7541 The Audiopedia
What is a Histogram?
 
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To understand graphs and ways to measure central tendency, please visit https://DontMemorise.com . Don’t Memorise brings learning to life through its captivating FREE educational videos. New videos every week. To stay updated, subscribe to our YouTube channel : http://bit.ly/DontMemoriseYouTube Register on our website to gain access to all videos and quizzes: http://bit.ly/DontMemoriseRegister Subscribe to our Newsletter: http://bit.ly/DontMemoriseNewsLetter Join us on Facebook: http://bit.ly/DontMemoriseFacebook Follow us : http://bit.ly/DontMemoriseBlog
Views: 127906 Don't Memorise
K-Mean Clustering
 
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Data Warehouse and Mining For more: http://www.anuradhabhatia.com
Views: 105701 Anuradha Bhatia
K-means clustering: how it works
 
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Full lecture: http://bit.ly/K-means The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively: (1) for each instance, we assign it to a cluster with the nearest centroid, and (2) we move each centroid to the mean of the instances assigned to it. The algorithm continues until no instances change cluster membership.
Views: 494437 Victor Lavrenko
What is Data Mining - Data Science Jargon for Beginners
 
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In this video I am going to give a simple and beginner definition of what data mining is in data analytics. The data science industry is very complicated, so I want to define data mining for you today. ► Full Playlist Explaining Data Jargon ( https://www.youtube.com/playlist?list=PL_9qmWdi19yDhnzqVCAhA4ALqDoqjeUOr ) ► http://jobsinthefuture.com/index.php/2017/11/25/what-is-data-mining-data-science-jargon-for-beginners/ Data Mining. This term is nearly self explanatory, but let's dig into it (haha, dig into it, data mining) and define data mining a little more to clarify any details. Data Miners explore large sets of data in order to discover patterns in the sets. Data miners look for patterns in order to define medical, buying habits, food shortages, etc... If you are going into the field of Data Analytics you will most certainly be doing a great deal of data mining. Data mining is a mass scale version of looking through thousands of people's daily biographies. What I mean by "looking through people's biographies" is you will be trying to understand how people are responding the the situation you are researching via data. Let's say your company releases a new drug to the market. This drug has been tested to stop the process of breakdown in joints that often leads to rheumatoid arthritis. Your drug ships out to 10,000 trial patients. Now you have a 10,000 person data set to manage. As the trial operates and the patients report their daily experience with the new drug you are being flooded with data about the drug. It is your job as the data miner to find the patterns and insights in order to accurately determine whether the drug is safe or not, the drug needs improvements, or perhaps the drug is not as effective as the company had hoped. In a nutshell data mining is a data analysts daily routine of researching data sets in order to learn from the data. Don't miss the Full review on Data Analytics defined and how to get a job! --- http://jobsinthefuture.com/index.php/2017/10/21/data-analyst-salary-and-how-to-become-a-data-analyst/ ------- SOCIAL Twitter ► @jobsinthefuture Facebook ►/jobsinthefuture Instagram ►@Jobsinthefuture WHERE I LEARN: (affiliate links) Lynda.com ► http://bit.ly/2rQB2u4 edX.org ► http://fxo.co/4y00 MY FAVORITE GEAR: (affiliate links) Camera ► http://amzn.to/2BWvE9o CamStand ► http://amzn.to/2BWsv9M Compute ► http://amzn.to/2zPeLvs Mouse ► http://amzn.to/2C0T9hq TubeBuddy ► https://www.tubebuddy.com/bengkaiser ► Download the Ultimate Guide Now! ( https://www.getdrip.com/forms/883303253/submissions/new ) Thanks for Supporting Our Channel! DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!
Views: 487 Ben G Kaiser
Data Mining - Clustering
 
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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.
Data Mining Lecture - - Finding frequent item sets | Apriori Algorithm | Solved Example (Eng-Hindi)
 
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In this video Apriori algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining in hindi, Finding frequent item sets, data mining, data mining algorithms in hindi, data mining lecture, data mining tools, data mining tutorial,
Views: 199636 Well Academy
K-Means Clustering Algorithm – Solved Numerical Question 1(Euclidean Distance)(Hindi)
 
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K-Means Clustering Algorithm – Solved Numerical Question 1(Euclidean Distance)(Hindi) Data Warehouse and Data Mining Lectures in Hindi
Top 5 Algorithms used in Data Science | Data Science Tutorial | Data Mining Tutorial | Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This tutorial will give you an overview of the most common algorithms that are used in Data Science. Here, you will learn what activities Data Scientists do and you will learn how they use algorithms like Decision Tree, Random Forest, Association Rule Mining, Linear Regression and K-Means Clustering. To learn more about Data Science click here: http://goo.gl/9HsPlv The topics related to 'R', Machine learning and Hadoop and various other algorithms have been extensively covered in our course “Data Science”. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 103313 edureka!
K-Means Clustering Algorithm - Cluster Analysis | Machine Learning Algorithm | Data Science |Edureka
 
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( Data Science Training - https://www.edureka.co/data-science ) This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4 Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #kmeans #clusteranalysis #clustering #datascience #machinelearning How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 63633 edureka!
Data- What is the Importance of DATA in Tamil?
 
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Data is collection of information . Data store and Data process Play List : https://www.youtube.com/playlist?list=PLLa_h7BriLH2U05m3eN43779AnrmieYHz YouTube channel link www.youtube.com/atozknowledgevideos Website http://atozknowledge.com/ Technology in Tamil & English
Views: 17311 atoz knowledge
KNIME: A Tool for Data Mining
 
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KNIME is very helpful tool for Data Mining tasks like Clustering, Classification, Standard Deviation and Mean
Views: 29485 Sania Habib
Meta data  in 5 mins hindi
 
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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://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [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: 105242 Last moment tuitions
Introduction to Data Mining: Data Cleaning
 
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In this Data Mining Fundamentals tutorial, we introduce Data Preprocessing, known as data cleaning, and the different strategies used to tackle it. There are many strategies for data preprocessing, and because data science is such a heterogeneous field, none of these strategies are strictly independent. -- At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 3600+ employees from over 742 companies globally, including many leaders in tech like Microsoft, Apple, and Facebook. -- Learn more about Data Science Dojo here: https://hubs.ly/H0f8M6d0 See what our past attendees are saying here: https://hubs.ly/H0f8Ln10 -- 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: 8387 Data Science Dojo
Spatial Data Mining I: Essentials of Cluster Analysis
 
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Whenever we look at a map, it is natural for us to organize, group, differentiate, and cluster what we see to help us make better sense of it. This session will explore the powerful Spatial Statistics techniques designed to do just that: Hot Spot Analysis and Cluster and Outlier Analysis. We will demonstrate how these techniques work and how they can be used to identify significant patterns in our data. We will explore the different questions that each tool can answer, best practices for running the tools, and strategies for interpreting and sharing results. This comprehensive introduction to cluster analysis will prepare you with the knowledge necessary to turn your spatial data into useful information for better decision making.
Views: 24655 Esri Events
What is UNSTRUCTURED DATA? What does UNSTRUCTURED DATA mean? UNSTRUCTURED DATA meaning
 
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What is UNSTRUCTURED DATA? What does UNSTRUCTURED DATA mean? UNSTRUCTURED DATA meaning - UNSTRUCTURED DATA definition - UNSTRUCTURED DATA 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 Unstructured data (or unstructured information) is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Unstructured information is typically text-heavy, but may contain data such as dates, numbers, and facts as well. This results in irregularities and ambiguities that make it difficult to understand using traditional programs as compared to data stored in fielded form in databases or annotated (semantically tagged) in documents. In 1998, Merrill Lynch cited a rule of thumb that somewhere around 80-90% of all potentially usable business information may originate in unstructured form. This rule of thumb is not based on primary or any quantitative research, but nonetheless is accepted by some. IDC and EMC project that data will grow to 40 zettabytes by 2020, resulting in a 50-fold growth from the beginning of 2010. The Computer World magazine states that unstructured information might account for more than 70%–80% of all data in organizations. The term is imprecise for several reasons: 1. Structure, while not formally defined, can still be implied. 2. Data with some form of structure may still be characterized as unstructured if its structure is not helpful for the processing task at hand. 3. Unstructured information might have some structure (semi-structured) or even be highly structured but in ways that are unanticipated or unannounced. Techniques such as data mining, natural language processing (NLP), and text analytics provide different methods to find patterns in, or otherwise interpret, this information. Common techniques for structuring text usually involve manual tagging with metadata or part-of-speech tagging for further text mining-based structuring. The Unstructured Information Management Architecture (UIMA) standard provided a common framework for processing this information to extract meaning and create structured data about the information. Software that creates machine-processable structure can utilize the linguistic, auditory, and visual structure that exist in all forms of human communication. Algorithms can infer this inherent structure from text, for instance, by examining word morphology, sentence syntax, and other small- and large-scale patterns. Unstructured information can then be enriched and tagged to address ambiguities and relevancy-based techniques then used to facilitate search and discovery. Examples of "unstructured data" may include books, journals, documents, metadata, health records, audio, video, analog data, images, files, and unstructured text such as the body of an e-mail message, Web page, or word-processor document. While the main content being conveyed does not have a defined structure, it generally comes packaged in objects (e.g. in files or documents, …) that themselves have structure and are thus a mix of structured and unstructured data, but collectively this is still referred to as "unstructured data". For example, an HTML web page is tagged, but HTML mark-up typically serves solely for rendering. It does not capture the meaning or function of tagged elements in ways that support automated processing of the information content of the page. XHTML tagging does allow machine processing of elements, although it typically does not capture or convey the semantic meaning of tagged terms. Since unstructured data commonly occurs in electronic documents, the use of a content or document management system which can categorize entire documents is often preferred over data transfer and manipulation from within the documents. Document management thus provides the means to convey structure onto document collections. Search engines have become popular tools for indexing and searching through such data, especially text.....
Views: 1307 The Audiopedia
k means clustering example HD
 
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This is not my work! Please give credits to the original author: https://vimeo.com/110060516 To calculate means from cluster centers: For example, if a cluster contains three data points such as {32,65}, {16,87} and {17,60}, the mean of this cluster is (32+16+17)/3 and (65+87+60)/3.
Views: 153035 Iulita
Binning | Binning Method | Binning Algorithm | Binning In Data Mining
 
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Binning |Binning Method | Binning Algorithm | Binning In Data Mining ************************************************ the binding of isaac, binning , binnington, equal width binning, binning method, binning algorithm, bin data in r, bin data in excel, binning in excel, binning in data mining, data mining, data mining techniques, data mining tutorial, data mining algorithms, data mining course, data mining excel, r data minin, python data mining, Please Subscribe My Channel
Views: 13619 Learning With Mahamud
K Means Clustering Data Mining Example | Machine Learning part 1
 
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K-means clustering algorithm is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. The problem is computationally difficult (NP-hard); however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both k-means and Gaussian mixture modeling. Additionally, they both use cluster centers to model the data; however, kmeans clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes. ====================================================== watch part 2 here: https://www.youtube.com/watch?v=AukQSbtZ1NQ book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar
Views: 21306 fun 2 code
Data Mining Lecture -- Decision Tree | Solved Example (Eng-Hindi)
 
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-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 174543 Well Academy
Last minute tutorials | k mean Clustering | ADBMS
 
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Please feel free to get in touch with me :) If it helped you, please like my facebook page and don't forget to subscribe to Last Minute Tutorials. Thaaank Youuu. Facebook: https://www.facebook.com/Last-Minute-Tutorials-862868223868621/ Website: www.lmtutorials.com For any queries or suggestions, kindly mail at: [email protected]
Views: 86646 Last Minute Tutorials
WHAT IS REGRESSION  ANALYSIS WITH EXAMPLES IN HINDI
 
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WHAT IS REGRESSION ANALYSIS WITH EXAMPLES IN HINDI
Views: 22104 LearnEveryone
What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning
 
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What is ANOMALY DETECTION? What does ANOMALY DETECTION mean? ANOMALY DETECTION meaning - ANOMALY DETECTION definition - ANOMALY DETECTION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. In data mining, anomaly detection (also outlier detection) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset.[1] Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.[2] In particular in the context of abuse and network intrusion detection, the interesting objects are often not rare objects, but unexpected bursts in activity. This pattern does not adhere to the common statistical definition of an outlier as a rare object, and many outlier detection methods (in particular unsupervised methods) will fail on such data, unless it has been aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro clusters formed by these patterns.[3] Three broad categories of anomaly detection techniques exist.[1] Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labeled as "normal" and "abnormal" and involves training a classifier (the key difference to many other statistical classification problems is the inherent unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behavior from a given normal training data set, and then testing the likelihood of a test instance to be generated by the learnt model.
Views: 5867 The Audiopedia
data mining fp growth | data mining fp growth algorithm | data mining fp tree example | fp growth
 
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In this video FP growth algorithm is explained in easy way in data mining Thank you for watching share with your friends Follow on : Facebook : https://www.facebook.com/wellacademy/ Instagram : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy data mining algorithms in hindi, data mining in hindi, data mining lecture, data mining tools, data mining tutorial, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining fp growth, data mining fp growth algorithm, data mining fp tree example, data mining fp tree example, fp growth tree data mining, fp tree algorithm in data mining, fp tree algorithm in data mining example, fp tree in data mining, data mining, fp growth algorithm, fp growth algorithm example, fp growth algorithm in data mining, fp growth algorithm in data mining example, fp growth algorithm in data mining examples ppt, fp growth algorithm in data mining in hindi, fp growth algorithm in r, fp growth english, fp growth example, fp growth example in data mining, fp growth frequent itemset, fp growth in data mining, fp growth step by step, fp growth tree
Views: 127282 Well Academy
Weka Data Mining Tutorial for First Time & Beginner Users
 
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23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.
Views: 449048 Brandon Weinberg
4. What is Integration (Hindi) |
 
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What is Integration and need for it.
Views: 370284 The Lighthouse
K-means clustering | Super easy explanation | in HINDI | CSVTU | Data-mining and warehousing
 
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Clustering is a data mining technique and we use k-means clustering to achieve this.. Its is grouping each value according to their characteristics..
Views: 280 Bunny funda
Data Mining Classification - Basic Concepts
 
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Classification in Data Mining with classification algorithms. Explanation on classification algorithm the decision tree technique with Example.
What is Spatial Data - An Introduction to Spatial Data and its Applications
 
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Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com Spatial Data, also referred to as geospatial data, is the information that identifies the geographic location of physical objects on Earth. It’s data that can be mapped, as it is stored as coordinates and topology. In this video, we introduce the concept of Spatial Data and break down the fundamentals of interacting with Spatial Data using common development tools. We then explore how these basics can be expanded upon in modern applications to assist in daily tasks, perform detailed analyses, or create interactive user experiences. Watch this video to learn: - What is Spatial Data - How and when to use Spatial Data - Spatial Data Examples and real-world applications
Views: 7959 Fullstack Academy
An Introduction to Linear Regression Analysis
 
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Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 713625 statisticsfun
OLAP vs OLTP in hindi
 
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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://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [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: 108273 Last moment tuitions
Data Mining Lecture -- Bayesian Classification | Naive Bayes Classifier | Solved Example (Eng-Hindi)
 
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In the bayesian classification The final ans doesn't matter in the calculation Because there is no need of value for the decision you have to simply identify which one is greater and therefore you can find the final result. -~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 157889 Well Academy
Mining of Road Accident Data Using K Means Clustering and Apriori Algorithm
 
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Introduction Road and accidents are uncertain and unsure incidents. In today’s world, traffic is increasing at a huge rate which leads to a large numbers of road accidents. Most of the road accident data analysis use data mining techniques, focusing on identifying factors that affect the severity of an accident. Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident. In this project, Apriori algorithm clubbed with Kmeans Clustering is used to analyse the road accidents factors Kmeans Algorithm The algorithm is composed of the following steps: It randomly chooses K points from the data set. Then it assigns each point to the group with closest centroid. It again recalculates the centroids. Assign each point to closest centroid. The process repeats until there is no change in the position of centroids. Apriori Algorithm Apriori involves frequent item-sets, which is a set of items appearing together in the given number of database records meeting the user-specified threshold. Apriori uses a bottom-up search method that creates every single frequent item-set. This means that to produce a frequent item-set of length; it must produce all of its subsets as need to be frequent. Follow Us: Facebook : https://www.facebook.com/E2MatrixTrainingAndResearchInstitute/ Twitter: https://twitter.com/e2matrix_lab/ LinkedIn: https://www.linkedin.com/in/e2matrix-thesis-jalandhar/ Instagram: https://www.instagram.com/e2matrixresearch/
Noob's Guide To Bitcoin Mining - Super Easy & Simple
 
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Some Helpful Links: • Buy Parts for a Mining Rig: http://amzn.to/2jSSsCz • Download NiceHash Miner: https://www.nicehash.com/?p=nhmintro • Choose a Wallet: https://goo.gl/d4QZVR • Coinbase: https://www.coinbase.com • Luno (in SA): https://www.luno.com/en/ • Kraken: https://www.kraken.com/ • Bitcoin.org: https://bitcoin.org/en/choose-your-wallet • Download Claymore Dual Eth Miner: https://goo.gl/SLNbDx • Download EWBF CUDA Zcash Miner: https://goo.gl/nqhuhL • Profitability Calculators: • https://www.nicehash.com/?p=calc • http://www.mycryptobuddy.com/ • https://www.cryptocompare.com/mining/calculator/ • Bitcoin News: http://www.coindesk.com/ • Support UFD via Bitcoin: 1G5n6qFafWSYf8CZikZTXXcThpoxKeXK1K Check out Wootware for all your PC rig needs: https://goo.gl/tTD16W Crosshair VI Hero: http://amzn.to/2rr7IWy Ryzen 7: http://amzn.to/2rHQnb5 Strix RX 580: http://amzn.to/2sKkZxk Dual RX 580: http://amzn.to/2rrnMrh Trident Z RGB: http://amzn.to/2prkDHI ASUS PCI WiFi Adapter: http://amzn.to/2teOAwm Enthoo Evolv: http://amzn.to/2oHZU5o HD120 RGB: http://amzn.to/2pfqYHQ Thermaltake Toughpower RGB 850W: http://amzn.to/2lSKNUv For the intro/outro music by Kalyptra: https://goo.gl/eVmyNVd Join the UFDisciple Discord server! - https://discord.gg/PApp82h My Twitter - http://www.twitter.com/ufdisciple My Facebook - http://www.facebook.com/ufdisciple My Instagram - http://www.instagram.com/ufdisciple
Views: 2002447 UFD Tech
Outlier Analysis - Part 1
 
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This video discusses about outliers and its possible cause.
Views: 17754 Gourab Nath