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Efficient Genetic Algorithm Based Data Mining Using Feature Selection with Hausdorff Distance

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Abstract

The development of powerful computers and faster input/output devices coupled with the need for storing and analyzing data have resulted in massive databases (of the order of terabytes). Such volumes of data clearly overwhelm more traditional data analysis methods. A new generation of tools and techniques are needed for finding interesting patterns in the data and discovering useful knowledge. In this paper we present the design of more effective and efficient genetic algorithm based data mining techniques that use the concepts of self-adaptive feature selection together with a wrapper feature selection method based on Hausdorff distance measure.

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Authors and Affiliations

  1. Department of Information Systems & OM, University of Texas at Arlington, P.O. Box 19437, Arlington, TX, 76019

    Riyaz Sikora & Selwyn Piramuthu

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  1. Riyaz Sikora
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  2. Selwyn Piramuthu
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Correspondence to Riyaz Sikora.

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Sikora, R., Piramuthu, S. Efficient Genetic Algorithm Based Data Mining Using Feature Selection with Hausdorff Distance. Inf Technol Manage 6, 315–331 (2005). https://doi.org/10.1007/s10799-005-3898-3

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