Contrast data mining : concepts, algorithms, and applications / edited by Guozhu Dong and James Bailey.

Format:

Book

Contributor:

Dong, Guozhu, 1957-

Bailey, James, 1971 June 30-

Series:

Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Chapman & Hall/CRC data mining and knowledge discovery series

Language:

English

Subjects (All):

Contrast data mining.

Data mining.

Physical Description:

1 online resource (428 p.)

Edition:

1st edition

Place of Publication:

Boca Raton : CRC Press, 2012.

Language Note:

English

System Details:

text file

Summary:

A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life ProblemsContrast Data Mining: Concepts, Algorithms, and Applications collects recent results from this specialized area of data mining that have previously been scattered in the literature, making them more accessible to researchers and developers in data mining and other fields. The book not only presents concepts and techniques for contrast data mining, but also explores the use of contrast mining to solve challenging problems in various scientific, medical, and business domain

Contents:

Front Cover; Contrast Data Mining: Concepts, Algorithms, and Applications; Copyright; Dedication; Table of Contents; Foreword; Preface; Part I: Preliminaries and Statistical Contrast Measures; 1. Preliminaries; 2. Statistical Measures for Contrast Patterns; Part II: Contrast Mining Algorithms; 3. Mining Emerging Patterns Using Tree Structures or Tree Based Searches; 4. Mining Emerging Patterns Using Zero-Suppressed Binary Decision Diagrams; 5. Efficient Direct Mining of Selective Discriminative Patterns for Classification; 6. Mining Emerging Patterns from Structured Data

7. Incremental Maintenance of Emerging PatternsPart III: Generalized Contrasts, Emerging Data Cubes, and Rough Sets; 8. More Expressive Contrast Patterns and Their Mining; 9. Emerging Data Cube Representations for OLAP Database Mining; 10. Relation Between Jumping Emerging Patterns and Rough Set Theory; Part IV: Contrast Mining for Classification & Clustering; 11. Overview and Analysis of Contrast Pattern Based Classification; 12. Using Emerging Patterns in Outlier and Rare-Class Prediction; 13. Enhancing Traditional Classifiers Using Emerging Patterns

14. CPC: A Contrast Pattern Based Clustering AlgorithmPart V: Contrast Mining for Bioinformatics and Chemoinformatics; 15. Emerging Pattern Based Rules Characterizing Subtypes of Leukemia; 16. Discriminating Gene Transfer and Microarray Concordance Analysis; 17. Towards Mining Optimal Emerging Patterns Amidst 1000s of Genes; 18. Emerging Chemical Patterns - Theory and Applications; 19. Emerging Patterns as Structural Alerts for Computational Toxicology; Part VI: Contrast Mining for Special Domains; 20. Emerging Patterns and Classification for Spatial and Image Data

21. Geospatial Contrast Mining with Applications on Labeled Spatial Data22. Mining Emerging Patterns for Activity Recognition; 23. Emerging Pattern Based Prediction of Heart Diseases and Powerline Safety; 24. Emerging Pattern Based Crime Spots Analysis and Rental Price Prediction; Part VII: Survey of Other Papers; 25. Overview of Results on Contrast Mining and Applications; Bibliography; Back Cover

Notes:

Description based upon print version of record.

Includes bibliographical references.

ISBN:

1-04-007191-0

0-429-16363-0

1-4398-5433-5

9780429163630

OCLC:

811507230