Contrast Data Mining: Concepts, Algorithms, and Applications

ISBN-13: 9781439854327 / Angielski / Twarda / 2012 / 434 str.

Guozhu Dong;James Bailey;Jinyan Li

Opis

A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life Problems
Contrast 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 domains. Learn from Real Case Studies of Contrast Mining Applications
In this volume, researchers from around the world specializing in architecture engineering, bioinformatics, computer science, medicine, and systems engineering focus on the mining and use of contrast patterns. They demonstrate many useful and powerful capabilities of a variety of contrast mining techniques and algorithms, including tree-based structures, zero-suppressed binary decision diagrams, data cube representations, and clustering algorithms. They also examine how contrast mining is used in leukemia characterization, discriminative gene transfer and microarray analysis, computational toxicology, spatial and image data classification, voting analysis, heart disease prediction, crime analysis, understanding customer behavior, genetic algorithms, and network security.

Recenzje

This book, edited by two leading researchers on contrast mining, Professors Guozhu Dong and James Bailey, and contributed to by over 40 data mining researchers and application scientists, is a comprehensive and authoritative treatment of this research theme. It presents a systematic introduction and a thorough overview of the state of the art for contrast data mining, including concepts, methodologies, algorithms, and applications. … the book will appeal to a wide range of readers, including data mining researchers and developers who want to be informed about recent progress in this exciting and fruitful area of research, scientific researchers who seek to find new tools to solve challenging problems in their own research domains, and graduate students who want to be inspired on problem solving techniques and who want to get help with identifying and solving novel data mining research problems in various domains.
—From the Foreword by Jiawei Han, University of Illinois, Urbana-Champaign, USA

Spis treści

Preliminaries and Statistical Contrast Measures. Contrast Mining Algorithms. Generalized Contrasts, Emerging Data Cubes, and Rough Sets. Contrast Mining for Classification and Clustering. Contrast Mining for Bioinformatics and Chemoinformatics. Contrast Mining for Special Domains. Survey of Other Papers. Bibliography. Index.