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Large-Scale Parallel Data Mining

  • Conference proceedings
  • © 2000

Overview

Editors:
  1. Mohammed J. Zaki

    1. Computer Science Department, Rensselaer Polytechnic Institute, Troy, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Ching-Tien Ho

    1. K55/B1, IBM Almaden Research Center, San Jose, USA

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 1759)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

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Table of contents (13 papers)

  1. Front Matter

    Pages I-VIII
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  2. Large-Scale Parallel Data Mining

    1. Parallel and Distributed Data Mining: An Introduction

      • Mohammed J. Zaki
      Pages 1-23

  3. Mining Frameworks

    1. 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 et al.
      Pages 24-54

    2. A High Performance Implementation of the Data Space Transfer Protocol (DSTP)

      • Stuart Bailey, Emory Creel, Robert Grossman, Srinath Gutti, Harinath Sivakumar
      Pages 55-64

    3. Active Mining in a Distributed Setting

      • Srinivasan Parthasarathy, Sandhya Dwarkadas, Mitsunori Ogihara
      Pages 65-82

  4. Associations and Sequences

    1. Efficient Parallel Algorithms for Mining Associations

      • Mahesh V. Joshi, Eui-Hong Sam Han, George Karypis, Vipin Kumar
      Pages 83-126

    2. Parallel Branch-and-Bound Graph Search for Correlated Association Rules

      • Shinichi Morishita, Akihiro Nakaya
      Pages 127-144

    3. Parallel Generalized Association Rule Mining on Large Scale PC Cluster

      • Takahiko Shintani, Masaru Kitsuregawa
      Pages 145-160

    4. Parallel Sequence Mining on Shared-Memory Machines

      • Mohammed J. Zaki
      Pages 161-189

  5. Classification

    1. Parallel Predictor Generation

      • D. B. Skillicorn
      Pages 190-196

    2. Efficient Parallel Classification Using Dimensional Aggregates

      • Sanjay Goil, Alok Choudhary
      Pages 197-210

    3. Learning Rules from Distributed Data

      • Lawrence O. Hall, Nitesh Chawla, Kevin W. Bowyer, W. Philip Kegelmeyer
      Pages 211-220

  6. Clustering

    1. Collective, Hierarchical Clustering from Distributed, Heterogeneous Data

      • Erik L. Johnson, Hillol Kargupta
      Pages 221-244

    2. A Data-Clustering Algorithm on Distributed Memory Multiprocessors

      • Inderjit S. Dhillon, Dharmendra S. Modha
      Pages 245-260

  7. Back Matter

    Pages 261-261
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Keywords

Editors and Affiliations

  • Computer Science Department, Rensselaer Polytechnic Institute, Troy, USA

    Mohammed J. Zaki

  • K55/B1, IBM Almaden Research Center, San Jose, USA

    Ching-Tien Ho

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