Swarm Inspired Approaches for K-prototypes clustering

Data clustering is a well-researched area in data mining and machine learning. The clustering algorithms that can handle both numeric and categorical variables have been extensively researched in the recent years. However, the clustering algorithms have a major limitation that converge to a local optima. Therefore, to address this problem this paper has proposed a novel algorithm ABC k-prototypes (Artificial Bee Colony clustering based on k-prototypes) for clustering mixed data. In our proposed approach we use the combination between the distribution centroid and the mean to calculate the dissimilarity between data objects and prototypes. The proposed algorithm is tested on five different datasets taken from the UCI machine learning data repository. The comparative results in the performance measures of the clustering showed that the proposed algorithm outperformed the traditional k-prototypes.