Large-Scale Parallel Data Mining

Mohammed J. Zaki

ISBN 10: 3540671943 / ISBN 13: 9783540671947

Published by Springer, Springer Feb 2000, 2000

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -Large-Scale Parallel Data Mining.- Parallel and Distributed Data Mining: An Introduction.- Mining Frameworks.- The Integrated Delivery of Large-Scale Data Mining: The ACSys Data Mining Project.- A High Performance Implementation of the Data Space Transfer Protocol (DSTP).- Active Mining in a Distributed Setting.- Associations and Sequences.- Efficient Parallel Algorithms for Mining Associations.- Parallel Branch-and-Bound Graph Search for Correlated Association Rules.- Parallel Generalized Association Rule Mining on Large Scale PC Cluster.- Parallel Sequence Mining on Shared-Memory Machines.- Classification.- Parallel Predictor Generation.- Efficient Parallel Classification Using Dimensional Aggregates.- Learning Rules from Distributed Data.- Clustering.- Collective, Hierarchical Clustering from Distributed, Heterogeneous Data.- A Data-Clustering Algorithm on Distributed Memory Multiprocessors.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 272 pp. Englisch.

Seller Inventory # 9783540671947

Bibliographic Details

• Title: Large-Scale Parallel Data Mining
• Publisher: Springer, Springer Feb 2000
• Publication Date: 2000
• Language: English
• ISBN 10: 3540671943
• ISBN 13: 9783540671947
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 15D millimeters 235 millimeters width by 155 millimeters height by 15 millimeters depth
• Weight: 417 grams

About this title

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.