Investigating stochastic diffusion search in data clustering

The use of clustering in various applications is key to its popularity in data analysis and data mining. Algorithms used for optimisation can be extended to perform clustering on a dataset. In this paper, a swarm intelligence technique - Stochastic Diffusion Search - is deployed for clustering purposes. This algorithm has been used in the past as a multi-agent global search and optimisation technique. In the context of this paper, the algorithm is applied to a clustering problem, tested on the classical Iris dataset and its performance is contrasted against nine other clustering techniques. The outcome of the comparison highlights the promising and competitive performance of the proposed method in terms of the quality of the solutions and its robustness in classification. This paper serves as a proof of principle of the novel applicability of this algorithm in the field of data clustering.