HYBRID ANT-BASED CLUSTERING ALGORITHM WITH CLUSTER ANALYSIS TECHNIQUES

Abstract

Cluster analysis is a data mining technology design ed to derive a good understanding of data to solve clustering problems by extracting useful informatio n from a large volume of mixed data elements. Recently, researchers have aimed to derive clustering algorit hms from nature’s swarm behaviors. Ant-based clustering is an approach inspired by the natural clustering a nd sorting behavior of ant colonies. In this resear ch, a hybrid ant-based clustering method is presented wit h new modifications to the original ant colony clus tering model (ACC) to enhance the operations of ants, pick ing 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 an d refinement process approaches are inspired by previous clustering methods, in addition to an adap tive threshold method. The results obtained show th at the hybrid ant-based clustering algorithm attains bette r 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.