Large-Scale Parallel Data Mining

✍ Scribed by Mohammed J. Zaki (auth.), Mohammed J. Zaki, Ching-Tien Ho (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2000
• Tongue: English
• Leaves: 269
• Series: Lecture Notes in Computer Science 1759 : Lecture Notes in Artificial Intelligence
• Edition: 1
• Category: Library

✦ Synopsis

With the unprecedented growth-rate at which data is being collected and stored electronically today in almost all fields of human endeavor, the efficient extraction of useful information from the data available is becoming an increasing scientific challenge and a massive economic need. This book presents thoroughly reviewed and revised full versions of papers presented at a workshop on the topic held during KDD'99 in San Diego, California, USA in August 1999 complemented by several invited chapters and a detailed introductory survey in order to provide complete coverage of the relevant issues. The contributions presented cover all major tasks in data mining including parallel and distributed mining frameworks, associations, sequences, clustering, and classification. All in all, the volume presents the state of the art in the young and dynamic field of parallel and distributed data mining methods. It will be a valuable source of reference for researchers and professionals.

✦ Table of Contents

Parallel and Distributed Data Mining: An Introduction....Pages 1-23
The Integrated Delivery of Large-Scale Data Mining: The ACSys Data Mining Project....Pages 24-54
A High Performance Implementation of the Data Space Transfer Protocol (DSTP)....Pages 55-64
Active Mining in a Distributed Setting....Pages 65-82
Efficient Parallel Algorithms for Mining Associations....Pages 83-126
Parallel Branch-and-Bound Graph Search for Correlated Association Rules....Pages 127-144
Parallel Generalized Association Rule Mining on Large Scale PC Cluster....Pages 145-160
Parallel Sequence Mining on Shared-Memory Machines....Pages 161-189
Parallel Predictor Generation....Pages 190-196
Efficient Parallel Classification Using Dimensional Aggregates....Pages 197-210
Learning Rules from Distributed Data....Pages 211-220
Collective, Hierarchical Clustering from Distributed, Heterogeneous Data....Pages 221-244
A Data-Clustering Algorithm on Distributed Memory Multiprocessors....Pages 245-260

✦ Subjects

Artificial Intelligence (incl. Robotics); Information Storage and Retrieval; Computer Communication Networks; Algorithm Analysis and Problem Complexity; Information Systems Applications (incl.Internet)