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Best books on Data Mining
 
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Best books on Data Mining
Views: 355 Books Magazines
Data Mining & Data Warehousing + 10 PYQs - GATE & UGC NET CS
 
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This live session will cover Data Mining & Data Warehousing + 10 PYQs - GATE & UGC NET CS. Subscribe GATE NoteBook For Class Notifications : https://www.youtube.com/channel/UC2UzX55u-PvWN_F_g_1DlTw?view_as=subscriber For FREE Notes , Join Facebook Group : https://www.facebook.com/groups/2057576547626004/
Views: 1916 GATE NoteBook
The Google Books Settlement: Data Mining and Non-Consumptive Use
 
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Session 1 of "The Google Books Settlement and the Future of Information Access". Moderator: Eric Kansa; Panelists: Peter Brantley, Director of Access, Internet Archive; Jim Pitman, Professor of Statistics, UC Berkeley. More information: http://www.ischool.berkeley.edu/newsandevents/events/20090828googlebooksconference
Views: 1015 UC Berkeley Events
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: 118448 LearnEveryone
Data mining
 
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Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, 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. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword, and is frequently also 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 popular 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", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1720 Audiopedia
▶ Application of Data Mining - Real Life Use of Data Mining - Where We Can Use Data Mining ?
 
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Data Mining becomes a very hot topic in this moments because of its various uses. We can apply data mining to predict about an event that might happen. ✔Application of Data Mining - Real Life Use of Data Mining - Where We Can Use Data Mining? We're gonna learn some real-life scenario of Data Mining in this video. »See Full #Data_Mining Video Series Here: https://www.youtube.com/watch?v=t8lSMGW5eT0&list=PL9qn9k4eqGKRRn1uBmEhlmEd58ATOziA1 In This Video You are gonna learn Data Mining #Bangla_Tutorial Data mining is an important process to discover knowledge about your customer behavior towards your business offerings. » My #Linkedin_Profile: https://www.linkedin.com/in/rafayet13 » Read My Full Article on #Data_Mining Career Opportunity & So On » Link: https://medium.com/@rafayet13 #Learn_Data_Mining_In_A_Easy_Way #Data_Mining_Essential_Course #Data_Mining_Course_For_Beginner ট্র্যাডিশনাল পদ্ধতিতে যে সকল সমস্যার সহজে কোন সমাধান দেয়া যায় না #ডেটা_মাইনিং ব্যবহারে সহজেই একটি সিদ্ধান্তে পৌঁছানো সম্ভব। আর সে সিদ্ধান্ত কাজে লাগিয়ে ব্যবসায়িক অথবা যে কোন সম্পর্কিত সিদ্ধান্ত গ্রহন সম্ভব। Data Mining,big data,data analysis,data mining tutorial,book bd,Bangla tutorials,data mining software,Data Mining,What is data mining,bookbd,data analysis,data mining tutorial,data science,big data, business intelligence,data mining tools,bangla tutorial,data mining bangla tutorial,how to,how to mine data, knowledge discovery, Artificial Intelligence,Deep learning,machine learning,Python tutorials, Data Mining in the Retail Industry What does the future of business look like? How data will transform business? How data mining will transform business?
Views: 9396 BookBd
▶ 5 Most Used Data Mining Software || Data Mining Tools -- Famous Data Mining Tools
 
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»See Full #Data_Mining Video Series Here: https://www.youtube.com/watch?v=t8lSMGW5eT0&list=PL9qn9k4eqGKRRn1uBmEhlmEd58ATOziA1 In This Video You are gonna learn Data Mining #Bangla_Tutorial Data mining is an important process to discover knowledge about your customer behavior towards your business offerings. » My #Linkedin_Profile: https://www.linkedin.com/in/rafayet13 » Read My Full Article on Data Mining Career Opportunity & So On » Link: https://medium.com/@rafayet13 #Learn_Data_Mining_In_A_Easy_Way #Data_Mining_Essential_Course #Data_Mining_Course_For_Beginner Here We're Going to Learn Which Software is best to use in Data Mining Field R remains the leading tool, with 49% share, but Python grows faster and almost catches up to R. RapidMiner remains the most popular general Data Science. আধুনিক প্রযুক্তির ব্যবহার বৃদ্ধির সাথে অতি দ্রুত ডেটা উৎপন্ন হচ্ছে। টেক জায়ান্ট আইবিএম জানায় ইন্টারনেটে যত ডেটা আছে তার ৯০ ভাগ উৎপন্ন হয়েছে গত তিন বছরে। এ ডেটা উৎপন্নের হার দিনকে দিন বেড়েই চলছে। বিশেষজ্ঞদের ধারনা ২০২০ সাল নাগাদ প্রায় ৪০ জেটাবাইট ডেটা জেনারেট হবে। যা ২০১১ তুলনায় প্রায় ৫০ গুন বেশি। বিশাল পরিমাণ এই ডেটা প্রক্রিয়াজাতের মাধ্যমে বিজ্ঞান, গবেষণা, চিকিৎসা, শিক্ষা ও ব্যবসায় ব্যপক ভুমিকা রাখা যেতে পারে। তাই বলা হচ্ছে “ বিগ ডেটা ইজ বিগ ইমপ্যাক্ট।” Data Mining,big data,data analysis,data mining tutorial,book , Bangla tutorials,data mining software,Data Mining,What is data mining, bookbd, data analysis,data mining tutorial,data science,big data,business tutorial,data mining Bangla tutorial,how to,how to mine data,knowledge discovery,Artificial Intelligence,Deep learning,machine learning,Python tutorials,
Views: 7741 BookBd
How data mining works
 
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Data mining concepts Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use.Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also 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. The term "data mining" is in fact 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 (e.g., machine learning) and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java[8] (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.[9] 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 semi-automatic or 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, sequential pattern 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.Data mining Data mining involves six common classes of tasks: Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation. Association rule learning (dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis. Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data. Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam". Regression – attempts to find a function which models the data with the least error that is, for estimating the relationships among data or datasets. Summarization – providing a more compact representation of the data set, including visualization and report generation.
Views: 692 Technology mart
references data mining
 
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book's references for data mining
Views: 2 Eko Endarmoko
Classification of Data Mining Problems v1
 
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I will explain 9 common data mining problem types. The information in this presentation is mostly based on the great book called "Data Science for Business" written by Provost and Fawcett. http://datascience.mertnuhoglu.com Please give positive or negative feedback on the presentation. Does it help? What do you suggest to make it better?
Views: 9782 Mert Nuhoglu
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: 8449 The Audiopedia
What is Data Mining? Explain Data Mining? [Hindi]
 
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SUBSCRIBE TO MY CHANNEL ►► https://www.youtube.com/c/ganeshbagaria BOOK ON DATA MINING ►► https://goo.gl/59xvuS Welcome to this video, In this video I will tell you about the Data Mining, about those marketing techniques and more. WHAT FULL PLAYLIST ► https://www.youtube.com/playlist?list=PLd43cTxFZWldk2PtkrW12WTH1eoyyi98P GAMING WITH ME ► http://bit.ly/ganofins MY BOOKS ►► MySQL for Beginners ► http://bit.ly/MySQLforBeginners HTML for Beginners ► http://bit.ly/HTMLforBeginners SECRET TOOL FOR YOUR CHANNEL'S GROWTH ► https://www.tubebuddy.com/ganofins SHOP USING THIS LINK TO SUPPORT THE CHANNEL ► http://www.amazon.com/?tag=ganofins-21 CONNECT WITH ME ►► Twitter ► https://twitter.com/ganofins Facebook ► https://facebook.com/ganofins Instagram ► https://instagram.com/ganofins Tumblr ► http://ganofins.tumblr.com GAMING WITH ME ► http://bit.ly/ganofins SUBSCRIBE TO MY CHANNEL ►► https://www.youtube.com/c/ganeshbagaria ||THANKS FOR WATCHING||
MATHS & STATISICS | Data mining tutorial from John Elder (4)
 
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Top tips for data mining success! Watch John Elder present this short tutorial on how to get ahead in data mining. This is extracted from training material produced by Elder Research, Inc. For more information about statistical analysis and data mining, check out the brand new reference book from Elsevier: The Handbook of Statistical Analysis and Data Mining Applications (www.elsevierdirect.com/datamining).
Views: 2775 Elsevier Books
Pocket Data Mining
 
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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
Data Mining Infographics
 
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This is a piece from the documentary I have produced mid this year on the subject, Data Mining. This was a collaboration between VEA (Melbourne) and Films Media Group (New York). Animated by Daniel Cowan and written by Cameron Moloney.
Views: 5930 Charulata Films
Learn Data Science in 3 Months
 
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I've created a 3 month curriculum to help you go from absolute beginner to proficient in the art of data science! This open source curriculum consists of purely free resources that I’ve compiled from across the Web and has no prerequisites, you don’t even have to have coded before. I’ve designed it for anyone who wants to improve their skills and find paid work ASAP, ether through a full-time position or contract work. You’ll be learning a host of tools like SQL, Python, Hadoop, and even data storytelling, all of which make up the complete data science pipeline. Curriculum for this video: https://github.com/llSourcell/Learn_Data_Science_in_3_Months Please Subscribe! And like. And comment. That's what keeps me going. Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Week 1 - Learn Python - EdX https://www.edx.org/course/introduction-python-data-science-2 - Siraj Raval https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU Week 2 - Statistics & Probability - KhanAcademy https://www.khanacademy.org/math/statistics-probability Week 3 - Data Pre-processing, Data Vis, Exploratory Data Analysis - EdX https://www.edx.org/course/introduction-to-computing-for-data-analysis Week 4 - Kaggle Project #1 Week 5-6 - Algorithms & Machine Learning - Columbia https://courses.edx.org/courses/course-v1:ColumbiaX+DS102X+2T2018/course/ Week 7 - Deep Learning - Part 1 and 2 of DL Book https://www.deeplearningbook.org/ - Siraj Raval https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3 Week 8 - Kaggle Project #2 Week 9 - Databases (SQL + NoSQL) - Udacity https://www.udacity.com/course/intro-to-relational-databases--ud197 - EdX https://www.edx.org/course/introduction-to-nosql-data-solutions-2 Week 10 - Hadoop & Map Reduce + Spark - Udacity https://www.udacity.com/course/intro-to-hadoop-and-mapreduce--ud617 - Spark Workshop https://stanford.edu/~rezab/sparkclass/slides/itas_workshop.pdf Week 11 - Data Storytelling - Edx https://www.edx.org/course/analytics-storytelling-impact-1 Week 12- Kaggle Project #3 Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hiring? Need a Job? See our job board!: www.theschool.ai/jobs/ Need help on a project? See our consulting group: www.theschool.ai/consulting-group/ Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 335234 Siraj Raval
MATHS & STATISTICS | Data mining tutorial from John Elder (3)
 
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Top tips for data mining success! Watch John Elder present this short tutorial on how to get ahead in data mining. This is extracted from training material produced by Elder Research, Inc. For more information about statistical analysis and data mining, check out the brand new reference book from Elsevier: The Handbook of Statistical Analysis and Data Mining Applications (www.elsevierdirect.com/datamining).
Views: 3271 Elsevier Books
Meta Brown: Data Mining for Dummies #DataTalk
 
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In our weekly #DataTalk, we had a chance to talk with Meta Brown about her work in data science and her latest book: Data Mining for Dummies. You can learn more about her by going to her website: http://www.metabrown.com/ You can read a full transcription of this video by going to: http://ex.pn/metabrown You can learn about upcoming #DataTalk events and tweetchats: http://experian.com/datatalk
Views: 1608 Experian
What is Data Mining
 
08:10
Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, 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 preprocessing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a buzzword, and is frequently misused to mean any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) but is also generalized to any kind of computer decision support system, including artificial intelligence, machine learning, and business intelligence. In the proper use of the word, the key term is discovery[citation needed], commonly defined as "detecting something new". Even the popular 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", or "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 are part of the data mining step, but do belong to the overall KDD process as additional steps.
Views: 52604 John Paul
MATHS & STATISTICS | Data mining tutorial from John Elder (1)
 
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Top tips for data mining success! Watch John Elder present this short tutorial on how to get ahead in data mining. This is extracted from training material produced by Elder Research, Inc. For more information about statistical analysis and data mining, check out the brand new reference book from Elsevier: The Handbook of Statistical Analysis and Data Mining Applications (www.elsevierdirect.com/datamining).
Views: 33334 Elsevier Books
fp growth algorithm basic example in data mining | how to construct fp tree
 
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ALL DATA MINING ALGORITHM videos are on below link : _____________________________________________________________ https://www.youtube.com/watch?v=JZepOmvB514&list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr ********************************************************************* apriori algorithm simple example : http://britsol.blogspot.in/2017/08/apriori-algorithm-example.html ____________________________________________________________ book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar
Views: 12203 fun 2 code
Data warehouse and Data Mining, IBPS SO IT officer, SBI SO, Campus interview
 
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This Video contains the basics of data warehouse and data mining, useful for IBPS SO IT officer Professional Knowledge
Views: 1266 Look Learn Lead
8/17/18 Using Analytic Solver Data Mining to Gain Insights from Your Data in Excel 1
 
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Live Webinar Recording: Do you want to learn and get results quickly from data mining and predictive analytics for your business? Have you found that "enterprise data mining" software involves far too much cost, risk and learning time? Do you want to apply traditional time series forecasting and regression, and also new data mining methods? Is the data you need found in SQL databases, data warehouses, public datasets, and Excel spreadsheets? Easily draw samples and build data mining models for all your data using Excel, PowerPivot and Analytic Solver Data Mining Use XLMiner's tools to uncover and visualize hidden relationships, clean and transform, and cluster your data Forecast future trends with a full range of traditional ARIMA, exponential smoothing, and regression methods Create classification and prediction models using the full spectrum of data mining methods -- from discriminant analysis and logistic regression to classification/regression trees, association rules, and neural networks
Views: 949 FrontlineSolvers
Agglomerative clustering dendrogram example data mining
 
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BOOK NAME : techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ ALL DATA MINING ALGORITHM VIDEOS ARE BELOW : https://www.youtube.com/watch?v=JZepOmvB514&list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ PDF OF THE SUM IS BELOW : http://britsol.blogspot.in/2017/11/agglomerative-clustering-dendrogram.html?m=1 $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ EXAMPLES ARE AT BELOW LINK http://britsol.blogspot.in/2017/08/apriori-algorithm-example.html $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$ DECISION TREE BASIC EXAMPLE PDF AND VIDEO ARE BELOW : VIDEO : https://www.youtube.com/watch?v=ajG5Yq1myMg&list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr&index=2 PDF : http://britsol.blogspot.in/2017/10/decision-tree-algorithm-pdf.html $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
Views: 4853 fun 2 code
The ART of Data Mining – Practical learnings from real-world data mining applications
 
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Machine Learning and data mining is part SCIENCE (ML algorithms, optimization), part ENGINEERING (large-scale modelling, real-time decisions), part PROCESS (data understanding, feature engineering, modelling, evaluation, and deployment), and part ART. In this talk, Dr. Shailesh Kumar focuses on the "ART of data mining" - the little things that make the big difference in the quality and sophistication of machine learning models we build. Using real-world analytics problems from a variety of domains, Shailesh shares a number of practical learnings in: (1) The art of understanding the data better - (e.g. visualization of text data in a semantic space) (2) The art of feature engineering - (e.g. converting raw inputs into meaningful and discriminative features) (3) The art of dealing with nuances in class labels - (e.g. creating, sampling, and cleaning up class labels) (4) The art of combining labeled and unlabelled data - (e.g. semi-supervised and active learning) (5) The art of decomposing a complex modelling problem into simpler ones - (e.g. divide and conquer) (6) The art of using textual features with structured features to build models, etc. The key objective of the talk is to share some of the learnings that might come in handy while "designing" and "debugging" machine learning solutions and to give a fresh perspective on why data mining is still mostly an ART.
Views: 2009 HasGeek TV
DATA SCIENCE FREE BOOKS ONLINE | DOWNLOAD YOUR COPY
 
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Here's a list of 10 must read book on Data Science & Machine Learning. Foundations of DATA SCIENCE Book www.cs.cornell.edu/jeh/book.pdf Understanding Machine Learning Book www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf The Elements of Statistical Learning Book web.stanford.edu/~hastie/Papers/ESLII.pdf An Introduction to Statistical Learning Book www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.pdf Mining of Massive Data Sets Book infolab.stanford.edu/~ullman/mmds/book.pdf
Views: 2216 DATA SCIENCE
The Library as Dataset: Text Mining at Million-Book Scale
 
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What do you do with a library? The large-scale digital collections scanned by Google and the Internet Archive have opened new ways to interact with books. The scale of digitization, however, also presents a challenge. We must find methods that are powerful enough to model the complexity of culture, but simple enough to scale to millions of books. In this talk I'll discuss one method, statistical topic modeling. I'll begin with an overview of the method. I will then demonstrate how to use such a model to measure changes over time and distinctions between sub-corpora. Finally, I will describe hypothesis tests that help us to distinguish consistent patterns from random variations. David Mimno is a postdoctoral researcher in the Computer Science department at Princeton University. He received his PhD from the University of Massachusetts, Amherst. Before graduate school, he served as Head Programmer at the Perseus Project, a digital library for cultural heritage materials, at Tufts University. He is supported by a CRA Computing Innovation fellowship.
Views: 2376 YaleUniversity
15 Best Free Books for Machine Learning | Download Link Available
 
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Books links on Amazon available here: https://tinyurl.com/yb94wz3k Free Books links available here: https://openload.co/f/IX6_AKTAcTQ https://openload.co/f/qe-5ppSbmGY https://openload.co/f/kfZ88_uvHG4 https://openload.co/f/F4UtH9JQkrA https://openload.co/f/gvz5ORAGtb0 On Amazon : 1.Machine Learning For Absolute Beginners: A Plain English Introduction (Second Edition) (Machine Learning For Beginners Book 1): https://amzn.to/2S6o6sF 2.Python Machine Learning By Example:https://amzn.to/2GsfmvP 3.Learning scikit-learn: Machine Learning in Python : https://amzn.to/2GtBllY 4.Machine Learning in Python : https://amzn.to/2STxNL3 5.Building Machine Learning Systems with Python : https://amzn.to/2S7MHxt 6.Beginning Python Visualization: Crafting Visual Transformation Scripts (Books for Professionals by Professionals) : https://amzn.to/2BwQqNM 7. Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists : https://amzn.to/2S6p34d 8.Machine Learning in Action : https://amzn.to/2BpDN7f 9.Mining The Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub and More : https://amzn.to/2S3armh 10.Advanced Machine Learning with Python : https://amzn.to/2GB5qzU 11.MACHINE LEARNING WITH PYTHON- An Approach to Applied Machine Learning : https://amzn.to/2CjMH7X 12. Machine Learning for Hackers: Case Studies and Algorithms to Get You Started : https://amzn.to/2S8jp1I 13. Vital Introduction to Machine Learning with Python: Best Practices to Improve and Optimize Machine Learning Systems and Algorithms (Computer Coding Book 2): https://amzn.to/2CkKoSb 14.Introduction to Machine Learning with Python: A Guide for Data Scientists :https://amzn.to/2GszTQH 15. Data Mining: Practical Machine Learning Tools and Techniques: https://amzn.to/2GoC3AU 16. Python: Advanced Predictive Analytics : https://amzn.to/2SapMl2 #freebooks #machinelearning #bestbooks
Views: 346 MLAIT
MATHS & STATISTICS | Data mining tutorial from John Elder (2)
 
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Top tips for data mining success! Watch John Elder present this short tutorial on how to get ahead in data mining. This is extracted from training material produced by Elder Research, Inc. For more information about statistical analysis and data mining, check out the brand new reference book from Elsevier: The Handbook of Statistical Analysis and Data Mining Applications (www.elsevierdirect.com/datamining).
Views: 16778 Elsevier Books
Data Mining: Mastering Data Mining Skills | Part - 2
 
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In this video, Qasim Ali Shah talking on the topic "DATA MINING SKILLS". In this session you will know about the content of trainers. He is giving some useful tips to all students, like: how should you can select your topic to speak effectively and after this what type of content will be helpful for your topic. You will know so many more after watching this video regarding above given topic. ===== ABOUT Qasim Ali Shah ===== Qasim Ali Shah is a Public Speaker- Teacher- Writer- Corporate Trainer & Leader for every age group- Businessmen- Corporate executives- Employees- Students- Housewives- Networkers- Sportsmen and for all who wish everlasting Success- Happiness- Peace and Personal Growth. He helps people to change their belief & thought pattern- experience less stress and more success in their lives through better communication- positive thinking and spiritual knowledge. ===== FOLLOW ME ON THE SOCIALS ===== - Qasim Ali Shah: https://goo.gl/6BKcxu - Google+: https://goo.gl/uPyGvT - Twitter: https://goo.gl/78MVoA - Website : https://goo.gl/Tgjy6u ===== Team Member: Waqas Nasir =====
Views: 8277 Qasim Ali Shah
CrossRef Metadata and Text and Data Mining
 
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CrossRef European Workshop in Vilnius 11 June 2015, the Lithuanian Academy of Sciences, Lithuania The workshop will consider good practice publishing for journals and books, how CrossRef services can improve digital journal and book quality, and ways to promote ethical publishing. It will also demonstrate how CrossRef services can enable digital journals and books to meet international publishing standards. The workshop has been developed from the highly successful annual meetings held in the US and UK and is being held in Vilnius to allow those unable to travel to the UK or US to participate. More information: http://serials.lt/crossref-european-workshop-in-vilnius/
Data Structures and Algorithms Complete Tutorial Computer Education for All
 
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Computer Education for all provides complete lectures series on Data Structure and Applications which covers Introduction to Data Structure and its Types including all Steps involves in Data Structures:- Data Structure and algorithm Linear Data Structures and Non-Linear Data Structure on Stack Data Structure on Arrays Data Structure on Queue Data Structure on Linked List Data Structure on Tree Data Structure on Graphs Abstract Data Types Introduction to Algorithms Classifications of Algorithms Algorithm Analysis Algorithm Growth Function Array Operations Two dimensional Arrays Three Dimensional Arrays Multidimensional arrays Matrix operations Operations on linked lists Applications of linked lists Doubly linked lists Introductions to stacks Operations on stack Array based implementation of stack Queue Data Structures Operations on Queues Linked list based implementation of queues Application of Trees Binary Trees Types of Binary Trees Implementation of Binary Trees Binary Tree Traversal Preorder Post order In order Binary Search Tree Introduction to Sorting Analysis of Sorting Algorithms Bubble Sort Selection Sort Insertion Sort Shell Sort Heap Sort Merge Sort Quick Sort Applications of Graphs Matrix representation of Graphs Implementations of Graphs Breadth First Search Topological Sorting Subscribe for More https://www.youtube.com/channel/UCiV37YIYars6msmIQXopIeQ Find us on Facebook: https://web.facebook.com/Computer-Education-for-All-1484033978567298 Java Programming Complete Tutorial for Beginners to Advance | Complete Java Training for all https://youtu.be/gg2PG3TwLx4
Data Mining Software in Healthcare
 
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This is a brief discussion of data mining software with an emphasis on the healthcare field.
Views: 4312 Joshua White
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: 21823 fun 2 code
DATA MINING: Predicting Tipping Points
 
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Tipping Points as evidenced in global events are, in many ways, influenced by media. DATA MINING for predicting and analyzing world events. This just released, ground-breaking book: DATA MINING: PREDICTING TIPPING POINTS by Dr. Philip Gordon, PhD, details three case studies which were selected on the basis of common Tipping Point Attributes: Each involved media contagiousness and stickiness during their development and, each arrived at a "dramatic moment in time", which could only be characterized by the phenomenon of Tipping Points. Three recent case studies explore the leading edge technologies of DATA MINING and the theory of TIPPING POINTS: The first case study, the 2008 Presidential Campaign of Barack Obama was chosen to examine a narrower scope and timeframe for the application of the analysis. In contrast to the second case study, the International Financial Crisis of 2007--2010, which involves a broader data study period to identify trends and more complex issues. The third study, Climate Change was included as consideration because the data mining research and analysis revealed critical relationships between Media Impact and Global Events. As the issue of Climate Change is still evolving, Dr. Gordon provides a Data Mining and Tipping Point Theory methodology for analyzing and predicting our planets' most pressing Global Tipping Points. Review Comments: "The genius of the formulation of DATA MINING: PREDICTING TIPPING POINTS is that it takes explicit account of the role of social media and the internet at facilitating bifurcations and promoting dynamical instability. In effect, we have trimmed a few feet of tail off the kite. As a reader, I was informed and educated as to the factors which conspire to influence stability / instability in complex social systems. ...the book does a good job of making sense of past bifurcations and dynamical instabilities, namely political instability, our perception of global climate change, and international economic crises...my compliments on a truly insightful Media Tipping Points." -Prof. Dr. (med.) Peter S. Geissler, A.B., B.S., M.S., M.Phil., Ph.D. (Yale) M.A., M.Eng., M.S., Ph.D., M.S., M.D., M.Phil.(Cantab) "A truly fascinating book that (teaches) a whole new way of thinking about major events and how the media can influence them. - Being a political junkie I was heavily into the media coverage of the 2008 Obama election and the global financial meltdown both via TV and the blogosphere. I now find myself looking for the tipping points and stickiness factors as other key events unfold. Usually, I have trouble reading theoretical books but this one was an easy read and if you want supporting data then the references are there. This could become a solid reference for those in the media who truly want to understand what they are reporting. Highly recommended and I look forward to Dr. Gordon's ongoing analysis of (future) events." -Dr. Ralph Moorhouse, Ph.D. Political junkie, Expert: natural polymers for industries "The application of Data Mining and Tipping Point Theory to media and global events, particularly the financial crisis and climate change, is a fascinating one." -Dr. Serge Besanger, PhD Expert, International Monetary Fund "...very interesting application (of the Tipping Point Theory)...potential opportunity for predicting other global events, i.e.: Egyptian crisis and perhaps, even terrorism activities." -Dr. Adam AJLANI, PhD Professor, Sciences Politic and Political Consultant, France TV1
Views: 82 BlueMatrixCatalog