期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2014
卷号：5
期号：2
页码：1076-1081
出版社：TechScience Publications
摘要：Data-Mining (DM) has become one of the most valuable tools for extracting and manipulating data and for establishing patterns in order to produce useful information for decision-making. Clustering is a data mining technique for finding important patterns in unorganized and huge data collections. This likelihood approach of clustering technique is quite often used by many researchers for classifications due to its’ being simple and easy to implement. In this work, we first use the Expectation-Maximization (EM) algorithm for sampling on the medical data obtained from Pima Indian Diabetes (PID) data set. This work is also based on comparative study of GA, ACO & PSO based Data Clustering methods. To Compare the results we use different metrics such as weighted arithmetic mean, standard deviation, Normalized absolute error & Precision value that measured the performance to compare and analyze the results. The results prove that the accuracy generated by using particle swarm optimization is more as compare to other optimization algorithms named as genetic algorithm and ant colony optimization algorithm in classification process. So, this work shows that the particle swarm optimization techniques results as the best optimization technique to handle the process
