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A Kernel-Based Membrane Clustering Algorithm

Jinyu Yang,Ru Chen,Guozhou Zhang,Hong Peng,Jun Wang,A. Riscos-Núñez

Published 2018 in Enjoying Natural Computing

ABSTRACT

The existing membrane clustering algorithms may fail to handle the data sets with non-spherical cluster boundaries. To overcome the shortcoming, this paper introduces kernel methods into membrane clustering algorithms and proposes a kernel-based membrane clustering algorithm, KMCA. By using non-linear kernel function, samples in original data space are mapped to data points in a high-dimension feature space, and the data points are clustered by membrane clustering algorithms. Therefore, a data clustering problem is formalized as a kernel clustering problem. In KMCA algorithm, a tissue-like P system is designed to determine the optimal cluster centers for the kernel clustering problem. Due to the use of non-linear kernel function, the proposed KMCA algorithm can well deal with the data sets with non-spherical cluster boundaries. The proposed KMCA algorithm is evaluated on nine benchmark data sets and is compared with four existing clustering algorithms.

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