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Trends in Applied Sciences Research
Year: 2012  |  Volume: 7  |  Issue: 2  |  Page No.: 103 - 117

Iterative Simulated Annealing for Medical Clustering Problems

R. Kittaneh, S. Abdullah and A. Abuhamdah    

Abstract: Clustering problem is a type of classification under optimisation problems, which is considered as a critical area of Data Mining. Medical clustering problem is a type of unsupervised learning in data mining. This study has presented an enhancement of K-Means (i.e., Multi K-Means) and iterative simulated annealing algorithm for solving medical clustering problems. The aim of this study was to improve the K-Means algorithm for a better performance and produce an effective algorithm for partitioning N objects into K clusters. The structure of the Iterative Simulated Annealing (ISA) algorithm resembles a Simulated Annealing (SA) algorithm structure. The basic difference is that, in ISA the temperature is reinitialized for further improvement, whilst, in SA the temperature is initialized only once at the beginning of the search. Therefore, ISA has a better capability of escaping from a local optima compared to SA and attempts to enhance the trial solution by exploring different neighborhood structures to overcome the limitation of the SA and by the swap mechanism ISA is able to get further improve. Experimental results obtained by three way of calculating the minimal distance that have been tested on six benchmark medical datasets that are available in UCI Machine Learning Repository show that, ISA algorithm with more computational time (coded as IISA) is able to produce significantly good quality solutions and outperformed SA and ISA algorithms on all datasets.

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