Large-Scale Parallel Data Mining

• Corporate Author: SpringerLink (Online service)
• Other Authors: Zaki, Mohammed J. (Editor, http://id.loc.gov/vocabulary/relators/edt), Ho, Ching-Tien (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2000.
• Edition: 1st ed. 2000.
• Series: Lecture Notes in Artificial Intelligence ; 1759
• Subjects: Artificial intelligence.; Database management.; Information storage and retrieval.; Computer communication systems.; Algorithms.; Application software.; Artificial Intelligence.; Database Management.; Information Storage and Retrieval.; Computer Communication Networks.; Algorithm Analysis and Problem Complexity.; Information Systems Applications (incl. Internet).

Table of Contents:

• 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.