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Induction as Pre-processing

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Methodologies for Knowledge Discovery and Data Mining (PAKDD 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1574))

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Abstract

In most data mining applications where induction is used as the primary tool for knowledge extraction, it is difficult to precisely identify a complete set of relevant attributes. The real world database from which knowledge is to be extracted usually contains a combination of relevant, noisy and irrelevant attributes. Therefore, pre-processing the database to select relevant attributes becomes a very important task in knowledge discovery and data mining. This paper starts with two existing induction systems, C4.5 and HCV, and uses one of them to select relevant attributes for the other. Experimental results on 12 standard data sets showtha t using HCV induction for C4.5 attribute selection is generally useful.

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Author information

Authors and Affiliations

  1. Department of Mathematical and Computer Sciences, Colorado School of Mines, 1500 Illinois Street, Golden, Colorado, 80401, USA

    Xindong Wu

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  1. Xindong Wu
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Systems Engineering, Yamaguchi University, Tokiwa-Dai, 2557, Ube, 755, Japan

    Ning Zhong

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Lizhu Zhou

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© 1999 Springer-Verlag Berlin Heidelberg

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Wu, X. (1999). Induction as Pre-processing. In: Zhong, N., Zhou, L. (eds) Methodologies for Knowledge Discovery and Data Mining. PAKDD 1999. Lecture Notes in Computer Science(), vol 1574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48912-6_16

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  • DOI: https://doi.org/10.1007/3-540-48912-6_16

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65866-5

  • Online ISBN: 978-3-540-48912-2

  • eBook Packages: Springer Book Archive

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