introduction to data mining pdf
Database / February 4, 2018

introduction to data mining pdf is book written by PANG NING TAN, MICHAEL STEINBACH, and VIPIN KUMAR in 2006 by  Pearson Education, Inc. This book consists of 792 pages and contains 10 chapters starting from Introduction than data, Exploring Data, Classification of Basic Concepts, Decision Trees, and Model Evaluation, Classification of Alternative Techniques, Association Analysis of Basic Concepts and Algorithms, Association Analysis of Advanced Concepts, Cluster Analysis of Basic Concepts and Algorithms, Cluster Analysis of Additional Issues and Algorithms, and  Anomaly Detection. Apart from these chapters there are five appendixes starting from A to E named as Linear Algebra, Dimensionality Reduction, Probability and Statistics, Regression, and Optimization. About the authors of introduction to data mining pdf: Dr Pang-Ning Tan is a Professor in the Department of Computer Science and Engineering at Michigan State University. He got his M.S. degree in Physics and Ph.D. degree in Computer Science from University of Minnesota. His exploration advantages center around the advancement of novel information digging calculations for an expansive scope of utilizations, including atmosphere and natural sciences, cybersecurity, and system examination. He has distributed more than 130 specialized papers in the territory of information mining, including top meetings and diaries, for example, KDD, ICDM, SDM, CIKM, and TKDE. He additionally filled in as partner editorial manager…

mongodb books pdf
Database / January 18, 2018

mongodb books pdf is a book authored by Kristina Chodorow Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472 Printed in the United States of America in May 2013. This contains 432 pages and consists of 23 chapters starting from Introduction and than Getting Started, Creating, Updating, and Deleting Documents, Querying, Indexing, Special Index and Collection Types, Aggregation, Application Design, Setting Up a Replica Set, Components of a Replica Set, Connecting to a Replica Set from Your Application, Administration, Introduction to Sharding, . Configuring Sharding, Choosing a Shard Key, Sharding Administration, Seeing What Your Application Is Doing, Data Administration, Durability, Starting and Stopping MongoDB, Monitoring MongoDB, Making Backups, and Deploying MongoDB. These chapters are divided into six sections starting from Introduction to MongoDB, than Designing Your Application, Replication, Sharding, Application Administration, and Server Administration. About the Authors of mongodb books pdf: Kristina Chodorow is a software engineer  who dealt with the MongoDB center for a long time. She drove MongoDB’s copy set advancement and in addition composing the PHP and Perl drivers. She has given chats on MongoDB at meetups and gatherings around the globe and keeps up a blog on specialized points at http://www.kchodorow.com. She at present works at Google. Conclusion of mongodb books pdf: Make a MongoDB group that will develop to address the issues of your application. With this short and compact book, you`ll get rules for…

introduction to big data pdf
Database / January 17, 2018

introduction to big data pdf is a book written by Judith Hurwitz, Alan Nugent, Dr. Fern Halper, and Marcia Kaufman published by John Wiley & Sons, Inc. in 2013. This book contains 339 pages and consists of 25 chapters starting from : Grasping the Fundamentals of Big Data,  Examining Big Data Types,  Old Meets New: Distributed Computing, Digging into Big Data Technology Components, Virtualization and How It Supports Distributed Computing, Examining the Cloud and Big Data, Operational Databases, MapReduce Fundamentals, Exploring the World of Hadoop, The Hadoop Foundation and Ecosystem, Appliances and Big Data Warehouses, Defining Big Data Analytics, Understanding Text Analytics and Big Data, Customized Approaches for Analysis of Big Data, Integrating Data Sources, Dealing with Real-Time Data Streams and Complex Event Processing, Operationalizing Big Data, Applying Big Data within Your Organization, Security and Governance for Big Data Environments,  The Importance of Big Data to Business, Analyzing Data in Motion: A Real-World View, Improving Business Processes with Big Data Analytics: A Real-World View, Ten Big Data Best Practices, Ten Great Big Data Resources, Ten Big Data Do’s and Don’ts. These chapters are divided into 7 modules starting from Getting Started with Big Data and than  Technology Foundations for Big Data, Big Data Management, Analytics and Big Data, Big Data Implementation, Big Data Solutions in the Real World, and The Part of Tens. About the authors of introduction…

mining of massive datasets
Database / January 16, 2018

mining of massive datasets is a book authored by Jure Leskovec, Anand Rajaraman, and Jeffrey D. Ullman published by Cambridge University Press in 2014. This book consists of 513 pages and contains twelve chapters starting from Data Mining and than Map Reduce and the New Software Stack, Finding Similar Items, Mining Data Streams, Link Analysis, Frequent Itemsets,  Clustering, Advertising on the Web, Advertising on the Web, Recommendation Systems, Mining Social-Network Graphs, Dimensionality Reduction, and Large-Scale Machine Learning and at the end a list indexes is given. About the authors of mining of massive datasets: Jure Leskovec got a Diploma in Computer Science from the University of Ljubljana, Slovenia, in 2004 and a PhD in Computational and Statistical Learning from the Carnegie Mellon University in 2008. He is a partner teacher of Computer Science at Stanford University concentrating on systems. He is the main researcher at Pinterest. In 2008/09 he was a postdoctoral specialist at Cornell University working with Jon Kleinberg and Dan Huttenlocher. He finished his Ph.D. in Machine Learning Department, School of Computer Science at Carnegie Mellon University under the supervision of Christos Faloutsos in 2008.He did his college degree in software engineering at University of Ljubljana, Slovenia in 2004. Likewise work with the Artificial Intelligence Laboratory, Jozef Stefan Institute, Ljubljana, Slovenia. Dr. Anand Rajaraman, PhD, is…

Data Mining for the Masses
Database / November 7, 2017

Data Mining for the Masses is written by Dr. Matthew North in 2012 licensed under common creative attribution 3.0  licensed. This book contains 3 sections and 14 chapters starting from introduction to data mining and CRISP-DM than organizational understanding and data understanding, data preparation, correlation, association rules, k-means clustering, discriminant analysis, linear regression, logistic regression, decisions trees, neural networks, text mining, evaluation and deployment, and data mining ethics. The three sections are data mining basics, data mining methods and models, and special consideration in data mining. About author of Data Mining for the Masses: Dr. Matt North came to the Information Systems and Technology department at UVU in 2015.  His teaching expertise is in Business Information Systems, Data Mining and Analytics, Geographic Information Systems (GIS) and Web/Mobile Software Development.  Research areas include Data Mining, GIS, Business & Technology Pedagogy, and Digital Ethics.  He is the author of two books, Life Lessons & Leadership (Agami Press, 2011), and Data Mining for the Masses (2nd Edition, Infinite Publishing, 2016), as well as numerous journal papers, articles, book chapters, and conference presentations.  An award winning professor and scholar, he is a Fulbright alumnus (Universidad Tecnológica Nacional de Argentina, 2013) and the recipient of the Ben Bauman Award for Excellence and the Gamma…