Simulated Annealing Fuzzy Clustering in Cancer Diagnosis

Classification is an important research area in cancer diagnosis. Fuzzy C-means (FCM) is one of the most widely used fuzzy clustering algorithms in real world applications. However there are two major limitations that exist in this method. The first is that a predefined number of clusters must be given in advance. The second is that the FCM technique can get stuck in sub-optimal solutions. In order to overcome these two limitations, Bandyopadhyay proposed a Variable String Length Simulated Annealing (VFC-SA) algorithm. Nevertheless, when this algorithm was implemented, it was found that sub-optimal solutions were still obtained in certain circumstances. In this paper, we propose an alternative fuzzy clustering algorithm, Simulated Annealing Fuzzy Clustering (SAFC), that improves and extends the ideas present in VFC-SA. The data from seven oral cancer patients tissue samples, obtained through Fourier Transform Infrared Spectroscopy (FTIR), were clustered using FCM, VFC-SA and the proposed SAFC algorithm. Experimental results are provided and comparisons are made to illustrate that the SAFC algorithm is able to find better clusters than the other two methods.