Introduction To Data Mining

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Introduction to Data Mining

Author : Pang-Ning Tan,Michael Steinbach,Vipin Kumar
Publisher : Pearson Education India
Page : 780 pages
File Size : 46,9 Mb
Release : 2016
Category : Electronic
ISBN : 9789332586055

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Introduction to Data Mining by Pang-Ning Tan,Michael Steinbach,Vipin Kumar Pdf

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. Each major topic is organized into two chapters, beginni

Introduction to Data Mining

Author : Pang-Ning Tan,Michael Steinbach,Anuj Karpatne,Vipin Kumar
Publisher : Unknown
Page : 864 pages
File Size : 50,7 Mb
Release : 2018-04-13
Category : Data mining
ISBN : 0273769227

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Introduction to Data Mining by Pang-Ning Tan,Michael Steinbach,Anuj Karpatne,Vipin Kumar Pdf

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.

Discovering Knowledge in Data

Author : Daniel T. Larose
Publisher : John Wiley & Sons
Page : 240 pages
File Size : 55,5 Mb
Release : 2005-01-28
Category : Computers
ISBN : 9780471687535

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Discovering Knowledge in Data by Daniel T. Larose Pdf

Learn Data Mining by doing data mining Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: * Data preprocessing and classification * Exploratory analysis * Decision trees * Neural and Kohonen networks * Hierarchical and k-means clustering * Association rules * Model evaluation techniques Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.

Introduction to Data Mining and Analytics

Author : Kris Jamsa
Publisher : Jones & Bartlett Learning
Page : 687 pages
File Size : 54,5 Mb
Release : 2020-02-03
Category : Computers
ISBN : 9781284180909

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Introduction to Data Mining and Analytics by Kris Jamsa Pdf

Data Mining and Analytics provides a broad and interactive overview of a rapidly growing field. The exponentially increasing rate at which data is generated creates a corresponding need for professionals who can effectively handle its storage, analysis, and translation.

Introduction to Data Mining and its Applications

Author : S. Sumathi,S.N. Sivanandam
Publisher : Springer
Page : 828 pages
File Size : 54,9 Mb
Release : 2006-10-12
Category : Computers
ISBN : 9783540343516

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Introduction to Data Mining and its Applications by S. Sumathi,S.N. Sivanandam Pdf

This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in database systems, and presents a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization.

Data Mining: Concepts and Techniques

Author : Jiawei Han,Micheline Kamber,Jian Pei
Publisher : Elsevier
Page : 740 pages
File Size : 49,8 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

Introduction to Algorithms for Data Mining and Machine Learning

Author : Xin-She Yang
Publisher : Academic Press
Page : 188 pages
File Size : 51,8 Mb
Release : 2019-07-15
Category : Mathematics
ISBN : 9780128172162

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Introduction to Algorithms for Data Mining and Machine Learning by Xin-She Yang Pdf

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

Machine Learning and Data Mining

Author : Igor Kononenko,Matjaz Kukar
Publisher : Horwood Publishing
Page : 484 pages
File Size : 54,8 Mb
Release : 2007-04-30
Category : Computers
ISBN : 1904275214

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Machine Learning and Data Mining by Igor Kononenko,Matjaz Kukar Pdf

Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. Written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining, this text is suitable foradvanced undergraduates, postgraduates and tutors in a wide area of computer science and technology, as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to libraries and bookshelves of the many companies who are using the principles of data mining to effectively deliver solid business and industry solutions.

INTRODUCTION TO DATA MINING WITH CASE STUDIES

Author : G. K. GUPTA
Publisher : PHI Learning Pvt. Ltd.
Page : 537 pages
File Size : 45,9 Mb
Release : 2014-06-28
Category : Computers
ISBN : 9788120350021

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INTRODUCTION TO DATA MINING WITH CASE STUDIES by G. K. GUPTA Pdf

The field of data mining provides techniques for automated discovery of valuable information from the accumulated data of computerized operations of enterprises. This book offers a clear and comprehensive introduction to both data mining theory and practice. It is written primarily as a textbook for the students of computer science, management, computer applications, and information technology. The book ensures that the students learn the major data mining techniques even if they do not have a strong mathematical background. The techniques include data pre-processing, association rule mining, supervised classification, cluster analysis, web data mining, search engine query mining, data warehousing and OLAP. To enhance the understanding of the concepts introduced, and to show how the techniques described in the book are used in practice, each chapter is followed by one or two case studies that have been published in scholarly journals. Most case studies deal with real business problems (for example, marketing, e-commerce, CRM). Studying the case studies provides the reader with a greater insight into the data mining techniques. The book also provides many examples, review questions, multiple choice questions, chapter-end exercises and a good list of references and Web resources especially those which are easy to understand and useful for students. A number of class projects have also been included.

Data Mining in Bioinformatics

Author : Jason T. L. Wang,Mohammed J. Zaki,Hannu Toivonen,Dennis Shasha
Publisher : Springer Science & Business Media
Page : 340 pages
File Size : 53,5 Mb
Release : 2006-03-30
Category : Computers
ISBN : 9781846280597

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Data Mining in Bioinformatics by Jason T. L. Wang,Mohammed J. Zaki,Hannu Toivonen,Dennis Shasha Pdf

Written especially for computer scientists, all necessary biology is explained. Presents new techniques on gene expression data mining, gene mapping for disease detection, and phylogenetic knowledge discovery.

Introduction to Data Mining

Author : Pang-Ning Tan,Michael Steinbach,Vipin Kumar
Publisher : Pearson Education India
Page : 780 pages
File Size : 43,6 Mb
Release : 2016
Category : Electronic
ISBN : 9789332586055

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Introduction to Data Mining by Pang-Ning Tan,Michael Steinbach,Vipin Kumar Pdf

Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. Each major topic is organized into two chapters, beginni

Cluster Analysis and Data Mining

Author : Ronald S. King
Publisher : Mercury Learning and Information
Page : 300 pages
File Size : 44,6 Mb
Release : 2015-05-12
Category : Computers
ISBN : 9781942270133

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Cluster Analysis and Data Mining by Ronald S. King Pdf

Cluster analysis is used in data mining and is a common technique for statistical data analysis used in many fields of study, such as the medical & life sciences, behavioral & social sciences, engineering, and in computer science. Designed for training industry professionals or for a course on clustering and classification, it can also be used as a companion text for applied statistics. No previous experience in clustering or data mining is assumed. Informal algorithms for clustering data and interpreting results are emphasized. In order to evaluate the results of clustering and to explore data, graphical methods and data structures are used for representing data. Throughout the text, examples and references are provided, in order to enable the material to be comprehensible for a diverse audience. A companion disc includes numerous appendices with programs, data, charts, solutions, etc. eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at [email protected]. FEATURES *Places emphasis on illustrating the underlying logic in making decisions during the cluster analysis *Discusses the related applications of statistic, e.g., Ward’s method (ANOVA), JAN (regression analysis & correlational analysis), cluster validation (hypothesis testing, goodness-of-fit, Monte Carlo simulation, etc.) *Contains separate chapters on JAN and the clustering of categorical data *Includes a companion disc with solutions to exercises, programs, data sets, charts, etc.

Data Mining: Introductory And Advanced Topics

Author : Margaret H Dunham
Publisher : Pearson Education India
Page : 332 pages
File Size : 43,8 Mb
Release : 2006-09
Category : Electronic
ISBN : 8177587854

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

Data Mining and Data Warehousing

Author : Parteek Bhatia
Publisher : Cambridge University Press
Page : 513 pages
File Size : 47,9 Mb
Release : 2019-06-27
Category : Computers
ISBN : 9781108727747

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Data Mining and Data Warehousing by Parteek Bhatia Pdf

Provides a comprehensive textbook covering theory and practical examples for a course on data mining and data warehousing.

The Handbook of Data Mining

Author : Nong Ye
Publisher : CRC Press
Page : 720 pages
File Size : 51,8 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.