A memetic algorithm for clustering with cluster based feature selection

Şener, İlyas Alper
Clustering is a well known unsupervised learning method which aims to group the similar data points and separate the dissimilar ones. Data sets that are subject to clustering are mostly high dimensional and these dimensions include relevant and redundant features. Therefore, selection of related features is a significant problem to obtain successful clusters. In this study, it is considered that relevant features for each cluster can be varied as each cluster in a data set is grouped by different set of features, so the problem is named as clustering with cluster based feature selection problem. We approach the problem as a center based clustering and three tasks which are selection of relevant features, decision of cluster centroid locations, and assignment of data points to the clusters are considered. The problem is combinatorially NP-Hard and mathematical models are not capable to solve large-size problems. Moreover, developed heuristics in the literature obtain results with high variance. Therefore, a metaheuristic framework which follows the problem characteristics is proposed. A memetic algorithm that integrates a genetic approach and neighborhood search is proposed to solve data sets with high number of data points. A modified version of this algorithm is also developed for high dimensional data sets. Proposed algorithms have been tested on different problem instances with different size and dimensions. Both simulated and real data sets are utilized for the tests. Experimental results have shown that the proposed approach obtains stable results with high accuracy and outperforms the state of the art.


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Citation Formats
İ. A. Şener, “A memetic algorithm for clustering with cluster based feature selection,” M.S. - Master of Science, Middle East Technical University, 2022.