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Implementation Issues in the Design of I/O Intensive Data Mining Applications on Clusters of Workstations

  • R. Baraglia
  • D. Laforenza
  • Salvatore Orlando
  • P. Palmerini
  • Raffaele Perego
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1800)

Abstract

This paper investigates scalable implementations of out-of-core I/O-intensive Data Mining algorithms on affordable parallel architectures, such as clusters of workstations. In order to validate our approach, the K-means algorithm, a well known DM Clustering algorithm, was used as a test case.

Keywords

Main Memory Implementation Issue Physical Memory Load Imbalance Data Ining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • R. Baraglia
    • 1
  • D. Laforenza
    • 1
  • Salvatore Orlando
    • 2
  • P. Palmerini
    • 1
  • Raffaele Perego
    • 1
  1. 1.Istituto CNUCEConsiglio Nazionale delle Ricerche (CNR)PisaItaly
  2. 2.Dipartimento di InformaticaUniversità Ca’ Fbscari di VeneziaItaly

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