Automatic kernel clustering with bee colony optimization algorithm

Abstract

Cluster analysis is important in data mining, especially if there is unsupervised data. Recently, many clustering methods have been proposed. Unfortunately, most of these require the definition of the number of clusters, in advance. This study addresses this weakness by proposing a new automatic clustering algorithm: automatic kernel clustering with bee colony optimization (AKC-BCO). AKC-BCO determines the appropriate number of clusters and assigns data points to correct clusters. This is accomplished by the kernel function, which increases clustering capability. This method is validated using several benchmark data sets. The result is compared with several existing automatic clustering methods. The experiment results demonstrate that the proposed AKC-BCO is more stable and accurate than others. Furthermore, the proposed method is also applied to a real-world medical problem.