Download or read book Data Mining written by Ian H. Witten and published by Elsevier. This book was released on 2011-02-03 with total page 665 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. - Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects - Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods - Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Download or read book Machine Learning and Data Mining written by Ryszad S. Michalski and published by Wiley. This book was released on 1998-04-22 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the new computational tools to get the most out of your information system. This practical guide, the first to clearly outline the situation for the benefit of engineers and scientists, provides a straightforward introduction to basic machine learning and data mining methods, covering the analysis of numerical, text, and sound data.

Download or read book MASTERING DATA MINING THE ART AND SCIENCE OF CUSTOMER RELATIONSHIP MANAGEMENT written by Michael J. A. Berry and published by . This book was released on 2008-09-01 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: Special Features: · Best-in-class data mining techniques for solving critical problems in all areas of business· Explains how to pick the right data mining techniques for specific problems· Shows how to perform analysis and evaluate results· Features real-world examples from across various industry sectors· Companion Web site with updates on data mining products and service providers About The Book: Companies have invested in building data warehouses to capture vast amounts of customer information. The payoff comes with mining or getting access to the data within this information gold mine to make better business decisions. Readers and reviewers loved Berry and Linoff's first book, Data Mining Techniques, because the authors so clearly illustrate practical techniques with real benefits for improved marketing and sales. Mastering Data Mining takes off from there-assuming readers know the basic techniques covered in the first book, the authors focus on how to best apply these techniques to real business cases. They start with simple applications and work up to the most powerful and sophisticated examples over the course of about 20 cases. (Ralph Kimball used this same approach in his highly successful Data Warehouse Toolkit). As with their first book, Mastering Data Mining is sufficiently technical for database analysts, but is accessible to technically savvy business and marketing managers. It should also appeal to a new breed of database marketing managers.

Download or read book Data Mining Concepts and Techniques written by Jiawei Han and published by Elsevier. This book was released on 2011-06-09 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. - Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects - Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields - Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

Download or read book The Top Ten Algorithms in Data Mining written by Xindong Wu and published by CRC Press. This book was released on 2009-04-09 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Identifying some of the most influential algorithms that are widely used in the data mining community, The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is wri

Download or read book Advances in Knowledge Discovery and Data Mining written by Usama M. Fayyad and published by . This book was released on 1996 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: Eight sections of this book span fundamental issues of knowledge discovery, classification and clustering, trend and deviation analysis, dependency derivation, integrated discovery systems, augumented database systems and application case studies. The appendices provide a list of terms used in the literature of the field of data mining and knowledge discovery in databases, and a list of online resources for the KDD researcher.

Download or read book Data Mining Methods and Models written by Daniel T. Larose and published by John Wiley & Sons. This book was released on 2006-02-02 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Apply powerful Data Mining Methods and Models to Leverage your Data for Actionable Results Data Mining Methods and Models provides: * The latest techniques for uncovering hidden nuggets of information * The insight into how the data mining algorithms actually work * The hands-on experience of performing data mining on large data sets Data Mining Methods and Models: * Applies a "white box" methodology, emphasizing an understanding of the model structures underlying the softwareWalks the reader through the various algorithms and provides examples of the operation of the algorithms on actual large data sets, including a detailed case study, "Modeling Response to Direct-Mail Marketing" * Tests the reader's level of understanding of the concepts and methodologies, with over 110 chapter exercises * Demonstrates the Clementine data mining software suite, WEKA open source data mining software, SPSS statistical software, and Minitab statistical software * Includes a companion Web site, www.dataminingconsultant.com, where the data sets used in the book may be downloaded, along with a comprehensive set of data mining resources. Faculty adopters of the book have access to an array of helpful resources, including solutions to all exercises, a PowerPoint(r) presentation of each chapter, sample data mining course projects and accompanying data sets, and multiple-choice chapter quizzes. With its emphasis on learning by doing, this is an excellent textbook for students in business, computer science, and statistics, as well as a problem-solving reference for data analysts and professionals in the field. An Instructor's Manual presenting detailed solutions to all the problems in the book is available onlne.

Download or read book Data Mining and Machine Learning written by Mohammed J. Zaki and published by Cambridge University Press. This book was released on 2020-01-30 with total page 779 pages. Available in PDF, EPUB and Kindle. Book excerpt: New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.

Download or read book Data Mining and Analysis written by Mohammed J. Zaki and published by Cambridge University Press. This book was released on 2014-05-12 with total page 607 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.

Download or read book Next Generation of Data Mining written by Hillol Kargupta and published by CRC Press. This book was released on 2008-12-24 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drawn from the US National Science Foundation's Symposium on Next Generation of Data Mining and Cyber-Enabled Discovery for Innovation (NGDM 07), Next Generation of Data Mining explores emerging technologies and applications in data mining as well as potential challenges faced by the field.Gathering perspectives from top experts across different di

Download or read book Data Mining written by Hillol Kargupta and published by . This book was released on 2004 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt: A state-of-the-art survey of recent advances in data mining or knowledge discovery.

Download or read book Data Mining written by Richard J. Roiger and published by CRC Press. This book was released on 2017-01-06 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides in-depth coverage of basic and advanced topics in data mining and knowledge discovery Presents the most popular data mining algorithms in an easy to follow format Includes instructional tutorials on applying the various data mining algorithms Provides several interesting datasets ready to be mined Offers in-depth coverage of RapidMiner Studio and Weka’s Explorer interface Teaches the reader (student,) hands-on, about data mining using RapidMiner Studio and Weka Gives instructors a wealth of helpful resources, including all RapidMiner processes used for the tutorials and for solving the end of chapter exercises. Instructors will be able to get off the starting block with minimal effort Extra resources include screenshot sequences for all RapidMiner and Weka tutorials and demonstrations, available for students and instructors alike The latest version of all freely available materials can also be downloaded at: http://krypton.mnsu.edu/~sa7379bt/

Download or read book Principles of Data Mining written by Max Bramer and published by Springer Science & Business Media. This book was released on 2007-03-06 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains the principal techniques of data mining: for classification, generation of association rules and clustering. It is written for readers without a strong background in mathematics or statistics and focuses on detailed examples and explanations of the algorithms given. This will benefit readers of all levels, from those who use data mining via commercial packages, right through to academic researchers. The book aims to help the general reader develop the necessary understanding to use commercial data mining packages, and to enable advanced readers to understand or contribute to future technical advances. Includes exercises and glossary.

Download or read book Data Mining with R written by Luis Torgo and published by CRC Press. This book was released on 2016-11-30 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining with R: Learning with Case Studies, Second Edition uses practical examples to illustrate the power of R and data mining. Providing an extensive update to the best-selling first edition, this new edition is divided into two parts. The first part will feature introductory material, including a new chapter that provides an introduction to data mining, to complement the already existing introduction to R. The second part includes case studies, and the new edition strongly revises the R code of the case studies making it more up-to-date with recent packages that have emerged in R. The book does not assume any prior knowledge about R. Readers who are new to R and data mining should be able to follow the case studies, and they are designed to be self-contained so the reader can start anywhere in the document. The book is accompanied by a set of freely available R source files that can be obtained at the book’s web site. These files include all the code used in the case studies, and they facilitate the "do-it-yourself" approach followed in the book. Designed for users of data analysis tools, as well as researchers and developers, the book should be useful for anyone interested in entering the "world" of R and data mining. About the Author Luís Torgo is an associate professor in the Department of Computer Science at the University of Porto in Portugal. He teaches Data Mining in R in the NYU Stern School of Business’ MS in Business Analytics program. An active researcher in machine learning and data mining for more than 20 years, Dr. Torgo is also a researcher in the Laboratory of Artificial Intelligence and Data Analysis (LIAAD) of INESC Porto LA.

Download or read book Data Mining X written by A. Zanasi and published by WIT Press. This book was released on 2009 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the end of the Cold War, the threat of large-scale wars has been substituted by new threats: terrorism, organised crime, trafficking, smuggling, proliferation of weapons of mass destruction. To react to them, a security strategy is necessary, but in order to be effective it requires several instruments, including technological tools. Consequently, research and development in the field of security is proving to be an ever-expanding field all over the world. Data mining is seen more and more not only as a key technology in business, engineering and science but as one of the key features in security. To stress that all these technologies must be seen as a way to improve not only the security of citizens but also their freedom, special attention will be given to data protection research issues. The 10th International Conference on Data Mining is part of the successful series and the topics include: Text mining and text analytics; Data mining applications; Data mining methods.

Download or read book Introduction to Algorithms for Data Mining and Machine Learning written by Xin-She Yang and published by Academic Press. This book was released on 2019-06-17 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages

Download or read book Exploratory Data Mining and Data Cleaning written by Tamraparni Dasu and published by John Wiley & Sons. This book was released on 2003-08-01 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written for practitioners of data mining, data cleaning and database management. Presents a technical treatment of data quality including process, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge. Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches. Uses case studies to illustrate applications in real life scenarios. Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques. Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining.