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RESEARCH PRODUCT

A novel heuristic memetic clustering algorithm

Asoke K. NandiB.g.w. CraenenTapani Ristaniemi

subject

ta113Determining the number of clusters in a data setBiclusteringClustering high-dimensional dataDBSCANComputingMethodologies_PATTERNRECOGNITIONTheoretical computer scienceCURE data clustering algorithmCorrelation clusteringCanopy clustering algorithmCluster analysisAlgorithmMathematics

description

In this paper we introduce a novel clustering algorithm based on the Memetic Algorithm meta-heuristic wherein clusters are iteratively evolved using a novel single operator employing a combination of heuristics. Several heuristics are described and employed for the three types of selections used in the operator. The algorithm was exhaustively tested on three benchmark problems and compared to a classical clustering algorithm (k-Medoids) using the same performance metrics. The results show that our clustering algorithm consistently provides better clustering solutions with less computational effort.

yearjournalcountryeditionlanguage
2013-09-012013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
https://doi.org/10.1109/mlsp.2013.6661948