A Suitability of Genetic Algorithm for Multidimensional Data Clustering
Classical Clustering algorithms which use calculus-based optimization methods can be trapped by local extrema in the process of optimizing the clustering criterion. They are also very sensitive to initialization. Unsupervised learning is useful in exploratory data analysis, image segmentation and some added class knowledge, can be used for classification as well. Evolutionary approaches, motivated by natural evolution, make use of evolutionary operators and a population of solutions to obtain the globally optimal partition of the data. Candidate solutions to the clustering problem are encoded as chromosomes. The most commonly used evolutionary operators are: selection, recombination, and mutation. Each transforms one or more input chromosomes into one or more output chromosomes. A fitness function evaluated on a chromosome determines a chromosome’s likelihood of surviving in the next generation. In this paper a Genetic Algorithm based Model for Data Clustering has been implemented in Matlab in a normal PC, and promising results were arrived. This approach can be directly applied to any clustering model which can be represented as a function dependent on a set of cluster centers. The approach can be further generalized for models that require parameters other than the cluster centers. The classification performance of the proposed system was tested with a huge multidimensional data set. Several tests were made on the system and overall significant results were achieved. The arrived results were significant and comparable.
Keywords: Data Mining, Data Classification, Clustering, Genetic Algorithm, Evolutionary Computing
Mrs M Punithavalli
Head of the Department,, Department of Computer Science, Sri Ramakrishna College of Arts and Science for Women
Guding M.Phil scholoars
Produced more than 20 research scholars.
Organised National symposiums.
Dr E Ramaraj
Department of Computer Science & Engineering, Alagappa University