Data Mining for Association Rules and Sequential Patterns: Sequential and Parallel Algorithms / Edition 1

by Jean-Marc Adamo

Overview

Recent advances in data collection, storage technologies, and computing power have made it possible for companies, government agencies and scientific laboratories to keep and manipulate vast amounts of data relating to their activities. This state-of-the-art monograph discusses essential algorithms for sophisticated data mining methods used with large-scale databases, focusing on two key topics: association rules and sequential pattern discovery. This will be an essential book for practitioners and professionals in computer science and computer engineering.

Product Details

ISBN-13:	9780387950488
Publisher:	Springer New York
Publication date:	12/28/2000
Edition description:	2001
Pages:	254
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

Introduction.- Search Space Partition-Based Rule Mining.- Apriori and Other Algorithms.- Mining for Rules Over Attribute Taxonomies.- Constraint-Based Rule Mining.- Data Partition-Based Rule Mining.- Mining Rules with Categorical and Metric Attributes.- Optimizing Rules with Quantitative Attributes.- Beyond Support-Confidence Framework.- Sequential Patterns: Search Space Partition-Based Mining.- Appendix.- References.- Index.