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Data Mining A Knowledge Discovery Approach
Author
Title
Data Mining [electronic resource] : A Knowledge Discovery Approach / by Krzysztof J. Cios, Witold Pedrycz, Roman W. Swiniarski, Lukasz Andrzej Kurgan.
ISBN
9780387367958
Edition
1st ed. 2007.
Publication
New York, NY : Springer US : Imprint: Springer, 2007.
Physical Description
1 online resource (XV, 606 p.)
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribe the sequence in which data mining projects should be performed. Data Mining offers an authoritative treatment of all development phases from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes this book from other texts in the area. It concentrates on data preparation, clustering and association rule learning (required for processing unsupervised data), decision trees, rule induction algorithms, neural networks, and many other data mining methods, focusing predominantly on those which have proven successful in data mining projects. Based upon the authors’ previous successful book on data mining and knowledge discovery, this new volume has been extensively expanded, making it an effective instructional tool for advanced-level undergraduate and graduate courses. This book offers: A suite of exercises at the end of every chapter, designed to enhance the reader’s understanding of the theory and proficiency with the tools presented Links to all-inclusive instructional presentations for each chapter to ensure easy use in classroom teaching Extensive appendices covering relevant mathematical material for convenient look-up Methods for addressing issues related to data privacy and security within the context of data mining, enabling the reader to balance potentially conflicting aims Summaries and bibliographical notes for each chapter, providing a broader perspective of the concepts and methods described Researchers, practitioners and students are certain to consider this volume an indispensable resource in successfully accomplishing the goals of their data mining projects. .
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
November 14, 2019
Contents
Data Mining and Knowledge Discovery Process
The Knowledge Discovery Process
Data Understanding
Data
Concepts of Learning, Classification, and Regression
Knowledge Representation
Data Preprocessing
Databases, Data Warehouses, and OLAP
Feature Extraction and Selection Methods
Discretization Methods
Data Mining: Methods for Constructing Data Models
Unsupervised Learning: Clustering
Unsupervised Learning: Association Rules
Supervised Learning: Statistical Methods
Supervised Learning: Decision Trees, Rule Algorithms, and Their Hybrids
Supervised Learning: Neural Networks
Text Mining
Data Models Assessment
Assessment of Data Models
Data Security and Privacy Issues
Data Security, Privacy and Data Mining.
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