چکیده (انگلیسی):

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.