Large-Scale Parallel Data Mining Book



Springer

Edited By:

Zaki, M.J., Rensselaer Polytechnic Institute, Troy, NY, USA
Ho, C.-T., IBM Research Center, San Jose, CA, USA

Large-Scale Parallel Data Mining

2000. VIII, 261 pp.
ISBN 3-540-67194-3
Softcover $39.00 (DM 68)

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.

Keywords: Data mining, information extraction, distributed information systems, parallel databases, internet searching, information retrieval, search algorithms

Series: Lecture Notes in Computer Science.
LNAI State-of-the-Art Survey, Volume 1759

Table of Contents

Parallel and Distributed Data Mining: An Introduction
            Mohammed J. Zaki

Mining Frameworks

The Integrated Delivery of Large-Scale Data Mining:  The ACSys Data Mining Project
            Graham Williams,  Irfan Altas,  Sergey Bakin,  Peter Christen, Markus Hegland, Alonso Marquez,
            Peter Milne.,  Rajehndra Nagappan,  Stephen Roberts

A High Performance Implementation of the Data Space Transfer Protocol (DSTP)
            S. Bailey, E. Creel, R Grossman, S. Gutti, H. Sivakumar

Active Mining in a Distributed Setting
            Srinivasan Parthasarathy, Sandhya Dwarkadas, Mitsunori Ogihara

Associations and Sequences

Efficient Parallel Algorithms for Mining Associations
            Mahesh V. Joshi, Eui-Hong (Sam) Han, George Karypis, Vipin Kumar

Parallel Branch-and-Bound Graph Search for Correlated Association Rules
            Shinichi Morishita, Akihiro Nakaya

Parallel Generalized Association Rule Mining on Large Scale PC Cluster
            Takahiko Shintani, Masaru Kitsuregawa

Parallel Sequence Mining on Shared-Memory Machines
            Mohammed J. Zaki

Classification

Parallel Predictor Generation
            David B. Skillicorn

Efficient Parallel Classification Using Dimensional Aggregates
            Sanjay Goil, Alok Choudhary

Learning Rules from Distributed Data
            Lawrence O. Hall, Nitesh Chawla, Kevin W. Bowyer, W. Philip Kegelmeyer

Clustering

Collective, Hierarchical Clustering from Distributed, Heterogeneous Data
            Erik L. Johnson, Hillol Kargupta

A Data-Clustering Algorithm on Distributed Memory Multiprocessors
            Inderjit S. Dhillon, Dharmendra S. Modha

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