Hybrid Ant-Based Clustering Algorithm with Cluster Analysis Techniques

Abstract

Cluster analysis is a data mining technology designed to derive a good understanding of data to solve clustering problems by extracting useful information from a large volume of mixed data elements. Recently, researchers have aimed to derive clustering algorithms from nature’s swarm behaviors. Ant-based clustering is an approach inspired by the natural clustering and sorting behavior of ant colonies. In this research, a hybrid ant-based clustering method is presented with new modifications to the original ant colony clustering model (ACC) to enhance the operations of ants, picking up and dropping off data items. Ants’ decisions are supported by operating two cluster analysis methods: Agglomerative Hierarchical Clustering (AHC) and density-based clustering. The proximity function and refinement process approaches are inspired by previous clustering methods, in addition to an adaptive threshold method. The results obtained show that the hybrid ant-based clustering algorithm attains better results than the ant-based clustering Handl model ATTA-C, k-means and AHC over some real and artificial datasets and the method requires less initial information about class numbers and dataset size.