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High Performance Data Mining

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High Performance Computing for Computational Science — VECPAR 2002 (VECPAR 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2565))

Abstract

Recent times have seen an explosive growth in the availability of various kinds of data. It has resulted in an unprecedented opportunity to develop automated data-driven techniques of extracting useful knowledge. Data mining, an important step in this process of knowledge discovery, consists of methods that discover interesting, non-trivial, and useful patterns hidden in the data [SAD+93, CHY96]. The field of data mining builds upon the ideas from diverse fields such as machine learning, pattern recognition, statistics, database systems, and data visualization. But, techniques developed in these traditional disciplines are often unsuitable due to some unique characteristics of today’s data-sets, such as their enormous sizes, high-dimensionality, and heterogeneity. There is a necessity to develop effective parallel algorithms for various data mining techniques. However, designing such algorithms is challenging, and the main focus of the paper is a description of the parallel formulations of two important data mining algorithms: discovery of association rules, and induction of decision trees for classification. We also briefly discuss an application of data mining to the analysis of large data sets collected by Earth observing satellites that need to be processed to better understand global scale changes in biosphere processes and patterns.

This work was supported by NSF CCR-9972519, by NASA grant # NCC 2 1231, by Army Research Office contract DA/DAAG55-98-1-0441, by the DOE grant LLNL/DOE B347714, and by Army High Performance Computing Research Center cooperative agreement number DAAD19-01-2-0014. Access to computing facilities was provided by AHPCRC and the Minnesota Supercomputer Institute. Related papers are available via WWW at URL: http://www.cs.umn.edu/~Rkumar.

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Authors and Affiliations

  1. University of Minnesota 4-192 EE/CSci Building, 200 Union Street SE, 55455, Minneapolis, MN, USA

    Vipin Kumar, Mahesh V. Joshi, Eui-Hong (Sam) Han, Pang-Ning Tan & Michael Steinbach

Authors
  1. Vipin Kumar
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  2. Mahesh V. Joshi
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  3. Eui-Hong (Sam) Han
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  4. Pang-Ning Tan
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  5. Michael Steinbach
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Editor information

Editors and Affiliations

  1. Faculdade de Engenharia da, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    José M. L. M. Palma  & A. Augusto Sousa  & 

  2. Department of Computer Science, University of Tennessee, 37996-1301, Knoxville, TN, USA

    Jack Dongarra

  3. Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Camino de Vera, s/n, Apartado 22012, 46020, Valencia, Spain

    Vicente Hernández

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© 2003 Springer-Verlag Berlin Heidelberg

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Kumar, V., Joshi, M.V., Han, EH.(., Tan, PN., Steinbach, M. (2003). High Performance Data Mining. In: Palma, J.M.L.M., Sousa, A.A., Dongarra, J., Hernández, V. (eds) High Performance Computing for Computational Science — VECPAR 2002. VECPAR 2002. Lecture Notes in Computer Science, vol 2565. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36569-9_8

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  • DOI: https://doi.org/10.1007/3-540-36569-9_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00852-1

  • Online ISBN: 978-3-540-36569-3

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