An Automatic Clustering For Interval Data Using The Genetic Algorithm

Abstract

This paper proposes an Automatic Clustering algorithm for Interval data using the Genetic algorithm (ACIG). In this algorithm, the overlapped distance between intervals is applied to determining the suitable number of clusters. Moreover, to optimize in clustering, we modify the Davies & Bouldin index, and to improve the crossover, mutation, and selection operators of the original genetic algorithm. The convergence of ACIG is theoretically proved and illustrated by the numerical examples. ACIG can be implemented effectively by the established Matlab procedure. Through the experiments on data sets with different characteristics, the proposed algorithm has shown the outstanding advantages in comparison to the existing ones. Recognizing the images by the proposed algorithm gives the potential in real applications of this research.