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2017 | OriginalPaper | Chapter

New Representations in Genetic Programming for Feature Construction in k-Means Clustering

Authors : Andrew Lensen, Bing Xue, Mengjie Zhang

Published in: Simulated Evolution and Learning

Publisher: Springer International Publishing

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Abstract

k-means is one of the fundamental and most well-known algorithms in data mining. It has been widely used in clustering tasks, but suffers from a number of limitations on large or complex datasets. Genetic Programming (GP) has been used to improve performance of data mining algorithms by performing feature construction—the process of combining multiple attributes (features) of a dataset together to produce more powerful constructed features. In this paper, we propose novel representations for using GP to perform feature construction to improve the clustering performance of the k-means algorithm. Our experiments show significant performance improvement compared to k-means across a variety of difficult datasets. Several GP programs are also analysed to provide insight into how feature construction is able to improve clustering performance.

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previous chapter Evolutionary Computation Theory for Remote Sensing Image Clustering: A Survey
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Metadata
Title
New Representations in Genetic Programming for Feature Construction in k-Means Clustering
Authors
Andrew Lensen
Bing Xue
Mengjie Zhang
Copyright Year
2017
Book
Simulated Evolution and Learning

Print ISBN: 978-3-319-68758-2

Electronic ISBN: 978-3-319-68759-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-68759-9_44

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