Hybridization Between Iterative Simulated Annealing and Modified Great Deluge for Medical Clustering Problems

Clustering is a type of classification under optimization problems, which is considered as a critical area of data mining. Medical clustering problem is a type of unsupervised learning in data mining. This work present a hybridization between our previous proposed Iterative Simulated Annealing (ISA) and Modified Great Deluge (MGD) algorithms for medical clustering problems. The aim of this work is to produce an effective algorithm for partitioning N objects into K clusters. The idea of the hybridization between MGD and ISA is to incorporate the strength of one approach with the strength of the other hoping a more promising algorithm. Also this combination can help to diverse the search space. Experimental results obtained two way of calculating the minimal distance that have been tested on six benchmark medical datasets show that, ISA-MGD is able to outperform some instances of MGD and ISA algorithms.