Data Mining: Concepts and Techniques

2011-06-09
Data Mining: Concepts and Techniques

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  • Author: Jiawei Han
  • Publisher: Elsevier
  • ISBN: 0123814804
  • Category : Computers
  • Languages : en
  • Pages : 740

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

Data Mining and Machine Learning

2020-01-30
Data Mining and Machine Learning

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  • Author: Mohammed J. Zaki
  • Publisher: Cambridge University Press
  • ISBN: 1108658695
  • Category : Computers
  • Languages : en
  • Pages : 780

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.

Data Mining and Analysis

2014-05-12
Data Mining and Analysis

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  • Author: Mohammed J. Zaki
  • Publisher: Cambridge University Press
  • ISBN: 0521766338
  • Category : Computers
  • Languages : en
  • Pages : 607

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

The Top Ten Algorithms in Data Mining

2009-04-09
The Top Ten Algorithms in Data Mining

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  • Author: Xindong Wu
  • Publisher: CRC Press
  • ISBN: 142008965X
  • Category : Business & Economics
  • Languages : en
  • Pages : 230

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

Data Mining

2011-02-03
Data Mining

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  • Author: Ian H. Witten
  • Publisher: Elsevier
  • ISBN: 0080890369
  • Category : Computers
  • Languages : en
  • Pages : 665

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

Handbook of Educational Data Mining

2010-10-25
Handbook of Educational Data Mining

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  • Author: Cristobal Romero
  • Publisher: CRC Press
  • ISBN: 9781439804582
  • Category : Business & Economics
  • Languages : en
  • Pages : 535

Handbook of Educational Data Mining (EDM) provides a thorough overview of the current state of knowledge in this area. The first part of the book includes nine surveys and tutorials on the principal data mining techniques that have been applied in education. The second part presents a set of 25 case studies that give a rich overview of the problems that EDM has addressed. Researchers at the Forefront of the Field Discuss Essential Topics and the Latest Advances With contributions by well-known researchers from a variety of fields, the book reflects the multidisciplinary nature of the EDM community. It brings the educational and data mining communities together, helping education experts understand what types of questions EDM can address and helping data miners understand what types of questions are important to educational design and educational decision making. Encouraging readers to integrate EDM into their research and practice, this timely handbook offers a broad, accessible treatment of essential EDM techniques and applications. It provides an excellent first step for newcomers to the EDM community and for active researchers to keep abreast of recent developments in the field.

Data Mining Techniques

2004-04-09
Data Mining Techniques

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  • Author: Michael J. A. Berry
  • Publisher: John Wiley & Sons
  • ISBN: 0471470643
  • Category : Business & Economics
  • Languages : en
  • Pages : 671

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.

Data Mining: Introductory And Advanced Topics

2006-09
Data Mining: Introductory And Advanced Topics

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  • Author: Margaret H Dunham
  • Publisher: Pearson Education India
  • ISBN: 9788177587852
  • Category :
  • Languages : en
  • Pages : 332

Mining of Massive Datasets

2014-11-13
Mining of Massive Datasets

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  • Author: Jure Leskovec
  • Publisher: Cambridge University Press
  • ISBN: 1107077230
  • Category : Computers
  • Languages : en
  • Pages : 480

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

Data Mining with R

2016-11-30
Data Mining with R

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  • Author: Luis Torgo
  • Publisher: CRC Press
  • ISBN: 1315399091
  • Category : Business & Economics
  • Languages : en
  • Pages : 426

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.