7. Conclusion

The present chapter notably includes the study of hybridization of popular clustering algorithms, K-Means and FCM, and identifies the best hybridization strategy. All experiments are carried out for segmenting four images, which include two medical images also. For all the algorithms CPU time, clustering fitness and sum of squared error (SSE) are taken into consideration while carrying out their performance evaluation. In all the experiments that are conducted, the proposed

hybrid algorithm KMandFCM is exhibiting better performance in terms of CPU time, Clustering Fitness (CF) and SSE.

In all experiments, it is also observed that the proposed hybrid clustering algorithms are showing better performance than the standard K-Means and FCM algorithms. Especially the KMandFCM algorithm has good results when compared to all other algorithms. Thus, it could be concluded that the hybrid clustering algorithm KMandFCM will have good application in other fields too.
