Novel Clustering Approach that Employs Genetic Algorithm with New Representation Scheme and Multiple Objectives

In this paper, we propose a new encoding scheme for GA and employ multiple objectives in handling the clustering problem. The proposed encoding scheme uses links so that objects to be clustered form a linear pseudo-graph. As multiple objectives are concerned, we used two objectives: 1) to minimize the Total Within Cluster Variation (TWCV); and 2) minimizing the number of clusters in a partition. Our approach obtains the optimal partitions for all the possible numbers of clusters in the Pareto Optimal set returned by a single GA run. The performance of the proposed approach has been tested using two well-known data sets: Iris and Ruspini. The obtained results demonstrate improvement over classical approaches.