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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2001: Advances in Knowledge Discovery and Data Mining pp 124–135Cite as

A Toolbox Approach to Flexible and Efficient Data Mining

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2035))

Abstract

This paper describes a flexible and efficient toolbox based on the scripting language Python, capable of handling common tasks in data mining. Using either a relational database or flat files the toolbox gives the user a uniform view of a data collection. Two core features of the toolbox are caching of database queries and parallelism within a collection of independent queries. Our toolbox provides a number of routines for basic data mining tasks on top of which the user can add more functions - mainly domain and data collection dependent - for complex and time consuming data mining tasks.

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Author information

Authors and Affiliations

  1. Australian National University, Canberra, ACT 0200, Australia

    Ole M. Nielsen⋆, Peter Christen, Markus Hegland & Tatiana Semenova

  2. James Cook University, Townsville, QLD 4811, Australia

    Timothy Hancock

Authors
  1. Ole M. Nielsen⋆
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  2. Peter Christen
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  3. Markus Hegland
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  4. Tatiana Semenova
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  5. Timothy Hancock
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Information Systems, The University of Hong Kong, Pokfulam, Hong Kong China

    David Cheung

  2. CSIRO Mathematical and Information Sciences, GPO Box 664, Canberra, ACT 2601, Australia

    Graham J. Williams

  3. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave., Kowloon, Hong Kong China

    Qing Li

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© 2001 Springer-Verlag Berlin Heidelberg

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Nielsen⋆, O.M., Christen, P., Hegland, M., Semenova, T., Hancock, T. (2001). A Toolbox Approach to Flexible and Efficient Data Mining. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_16

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  • DOI: https://doi.org/10.1007/3-540-45357-1_16

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41910-5

  • Online ISBN: 978-3-540-45357-4

  • eBook Packages: Springer Book Archive

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