Data Mining

Author : Jiawei Han,Micheline Kamber,Jian Pei
Publisher : Elsevier
Page : 740 pages
File Size : 53,7 Mb
Release : 2011-06-09
Category : Computers
ISBN : 9780123814807

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Data Mining: Concepts and Techniques by Jiawei Han,Micheline Kamber,Jian Pei Pdf

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

Author : Mohammed J. Zaki,Wagner Meira, Jr
Publisher : Cambridge University Press
Page : 779 pages
File Size : 51,6 Mb
Release : 2020-01-30
Category : Business & Economics
ISBN : 9781108473989

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Data Mining and Machine Learning by Mohammed J. Zaki,Wagner Meira, Jr Pdf

New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.

Author : Ian H. Witten,Eibe Frank,Mark A. Hall
Publisher : Elsevier
Page : 665 pages
File Size : 52,5 Mb
Release : 2011-02-03
Category : Computers
ISBN : 9780080890364

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Data Mining by Ian H. Witten,Eibe Frank,Mark A. Hall Pdf

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

Author : Salvador García,Julián Luengo,Francisco Herrera
Publisher : Springer
Page : 320 pages
File Size : 53,6 Mb
Release : 2014-08-30
Category : Technology & Engineering
ISBN : 9783319102474

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Data Preprocessing in Data Mining by Salvador García,Julián Luengo,Francisco Herrera Pdf

Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.

Author : Jure Leskovec,Anand Rajaraman,Jeffrey David Ullman
Publisher : Cambridge University Press
Page : 480 pages
File Size : 40,8 Mb
Release : 2014-11-13
Category : Computers
ISBN : 9781107077232

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Mining of Massive Datasets by Jure Leskovec,Anand Rajaraman,Jeffrey David Ullman Pdf

Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.

Author : Mohammed J. Zaki,Wagner Meira, Jr,Wagner Meira
Publisher : Cambridge University Press
Page : 607 pages
File Size : 49,8 Mb
Release : 2014-05-12
Category : Computers
ISBN : 9780521766333

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Data Mining and Analysis by Mohammed J. Zaki,Wagner Meira, Jr,Wagner Meira Pdf

A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.

Author : Michael J. A. Berry,Gordon S. Linoff
Publisher : John Wiley & Sons
Page : 671 pages
File Size : 49,6 Mb
Release : 2004-04-09
Category : Business & Economics
ISBN : 9780471470649

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Data Mining Techniques by Michael J. A. Berry,Gordon S. Linoff Pdf

Many companies have invested in building large databases and data warehouses capable of storing vast amounts of information. This book offers business, sales and marketing managers a practical guide to accessing such information.

Author : Charu C. Aggarwal
Publisher : Springer
Page : 734 pages
File Size : 50,6 Mb
Release : 2015-04-13
Category : Computers
ISBN : 9783319141428

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Data Mining by Charu C. Aggarwal Pdf

This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks. Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories: Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems. Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data. Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples. Praise for Data Mining: The Textbook - “As I read through this book, I have already decided to use it in my classes. This is a book written by an outstanding researcher who has made fundamental contributions to data mining, in a way that is both accessible and up to date. The book is complete with theory and practical use cases. It’s a must-have for students and professors alike!" -- Qiang Yang, Chair of Computer Science and Engineering at Hong Kong University of Science and Technology "This is the most amazing and comprehensive text book on data mining. It covers not only the fundamental problems, such as clustering, classification, outliers and frequent patterns, and different data types, including text, time series, sequences, spatial data and graphs, but also various applications, such as recommenders, Web, social network and privacy. It is a great book for graduate students and researchers as well as practitioners." -- Philip S. Yu, UIC Distinguished Professor and Wexler Chair in Information Technology at University of Illinois at Chicago

Author : Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniarski,Lukasz Andrzej Kurgan
Publisher : Springer Science & Business Media
Page : 606 pages
File Size : 55,7 Mb
Release : 2007-10-05
Category : Computers
ISBN : 9780387367958

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Data Mining by Krzysztof J. Cios,Witold Pedrycz,Roman W. Swiniarski,Lukasz Andrzej Kurgan Pdf

This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material.

Author : Nong Ye
Publisher : CRC Press
Page : 720 pages
File Size : 43,6 Mb
Release : 2003-04-01
Category : Computers
ISBN : 9781410607515

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The Handbook of Data Mining by Nong Ye Pdf

Created with the input of a distinguished International Board of the foremost authorities in data mining from academia and industry, The Handbook of Data Mining presents comprehensive coverage of data mining concepts and techniques. Algorithms, methodologies, management issues, and tools are all illustrated through engaging examples and real-world applications to ease understanding of the materials. This book is organized into three parts. Part I presents various data mining methodologies, concepts, and available software tools for each methodology. Part II addresses various issues typically faced in the management of data mining projects and tips on how to maximize outcome utility. Part III features numerous real-world applications of these techniques in a variety of areas, including human performance, geospatial, bioinformatics, on- and off-line customer transaction activity, security-related computer audits, network traffic, text and image, and manufacturing quality. This Handbook is ideal for researchers and developers who want to use data mining techniques to derive scientific inferences where extensive data is available in scattered reports and publications. It is also an excellent resource for graduate-level courses on data mining and decision and expert systems methodology.

Author : Soumen Chakrabarti,Richard E. Neapolitan,Dorian Pyle,Mamdouh Refaat,Markus Schneider,Toby J. Teorey,Ian H. Witten,Earl Cox,Eibe Frank,Ralf Hartmut Güting,Jiawei Han,Xia Jiang,Micheline Kamber,Sam S. Lightstone,Thomas P. Nadeau
Publisher : Morgan Kaufmann
Page : 477 pages
File Size : 48,8 Mb
Release : 2008-10-31
Category : Computers
ISBN : 9780080877884

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Data Mining: Know It All by Soumen Chakrabarti,Richard E. Neapolitan,Dorian Pyle,Mamdouh Refaat,Markus Schneider,Toby J. Teorey,Ian H. Witten,Earl Cox,Eibe Frank,Ralf Hartmut Güting,Jiawei Han,Xia Jiang,Micheline Kamber,Sam S. Lightstone,Thomas P. Nadeau Pdf

This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology. The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining. This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources. Chapters contributed by various recognized experts in the field let the reader remain up to date and fully informed from multiple viewpoints. Presents multiple methods of analysis and algorithmic problem-solving techniques, enhancing the reader’s technical expertise and ability to implement practical solutions. Coverage of both theory and practice brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases.

Author : Hillol Kargupta,Jiawei Han,Philip S. Yu,Rajeev Motwani,Vipin Kumar
Publisher : CRC Press
Page : 601 pages
File Size : 46,7 Mb
Release : 2008-12-24
Category : Computers
ISBN : 1420085875

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Next Generation of Data Mining by Hillol Kargupta,Jiawei Han,Philip S. Yu,Rajeev Motwani,Vipin Kumar Pdf

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 disciplines, the book debates upcoming challenges and outlines computational methods. The contributors look at how ecology, astronomy, social science, medicine, finance, and more can benefit from the next generation of data mining techniques. They examine the algorithms, middleware, infrastructure, and privacy policies associated with ubiquitous, distributed, and high performance data mining. They also discuss the impact of new technologies, such as the semantic web, on data mining and provide recommendations for privacy-preserving mechanisms. The dramatic increase in the availability of massive, complex data from various sources is creating computing, storage, communication, and human-computer interaction challenges for data mining. Providing a framework to better understand these fundamental issues, this volume surveys promising approaches to data mining problems that span an array of disciplines.

Author : Luis Torgo
Publisher : CRC Press
Page : 426 pages
File Size : 48,7 Mb
Release : 2016-11-30
Category : Business & Economics
ISBN : 9781315399096

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Data Mining with R by Luis Torgo Pdf

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.

Author : Margaret H Dunham
Publisher : Pearson Education India
Page : 332 pages
File Size : 54,9 Mb
Release : 2006-09
Category : Electronic
ISBN : 8177587854

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Data Mining: Introductory And Advanced Topics by Margaret H Dunham Pdf

Author : David Nettleton
Publisher : Elsevier
Page : 304 pages
File Size : 40,9 Mb
Release : 2014-01-29
Category : Computers
ISBN : 9780124166585

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Commercial Data Mining by David Nettleton Pdf

Whether you are brand new to data mining or working on your tenth predictive analytics project, Commercial Data Mining will be there for you as an accessible reference outlining the entire process and related themes. In this book, you'll learn that your organization does not need a huge volume of data or a Fortune 500 budget to generate business using existing information assets. Expert author David Nettleton guides you through the process from beginning to end and covers everything from business objectives to data sources, and selection to analysis and predictive modeling. Commercial Data Mining includes case studies and practical examples from Nettleton's more than 20 years of commercial experience. Real-world cases covering customer loyalty, cross-selling, and audience prediction in industries including insurance, banking, and media illustrate the concepts and techniques explained throughout the book. Illustrates cost-benefit evaluation of potential projects Includes vendor-agnostic advice on what to look for in off-the-shelf solutions as well as tips on building your own data mining tools Approachable reference can be read from cover to cover by readers of all experience levels Includes practical examples and case studies as well as actionable business insights from author's own experience