Abstract
Medical data clustering is popular scientific approach for finding hidden patterns from large medical dataset. Medical experts utilised these patterns to make clinical diagnosis for likelihood of a disease. Clustering groups the data objects of dataset into different groups based on data similarity within group is higher than other groups. In this work, a hybrid PSO-GA algorithm is developed for medical data clustering based on 'particle swarm optimisation (PSO) and genetic algorithm (GA)'. Hybrid PSO-GA performance has examined against K-means, PSO and GA with respect to six popular medical datasets namely iris, thyroid, breast cancer, heart, diabetes and pima adopted from UCI machine learning repository over three criterion namely, i.e., sum of intra cluster distance, error rate and CPU running time. Tabular and graphical results of simulation confirm hybrid PSO-GA technique for medical data clustering is superior against K-means, PSO and GA.
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