Chaotic ant swarm approach for data clustering

Clustering divides data into meaningful or useful groups (clusters) without any prior knowledge. It is a key technique in data mining and has become an important issue in many fields. This article presents a new clustering algorithm based on the mechanism analysis of chaotic ant swarm (CAS). It is an optimization methodology for clustering problem which aims to obtain global optimal assignment by minimizing the objective function. The proposed algorithm combines three advantages into one: finding global optimal solution to the objective function, not sensitive to clusters with different size and density and suitable to multi-dimensional data sets. The quality of this approach is evaluated on several well-known benchmark data sets. Compared with the popular clustering method named k-means algorithm and the PSO-based clustering technique, experimental results show that our algorithm is an effective clustering technique and can be used to handle data sets with complex cluster sizes, densities and multiple dimensions.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► We employ chaotic ant swarm (CAS) to clustering problems. ► Clustering can be seen as an optimization problem by optimizing SSE. ► Data sets with complex cluster sizes, densities and multiple dimensions can be clustered with high quality. ► CAS-C is a more stable clustering technique and exhibits better convergence than the k-means and PSO-based clustering algorithms.