Agglomerative Fuzzy Clustering based on Bayesian Interpretation

This paper presents iterative Bayesian fuzzy clustering (IBFC), which is based on incorporating integrated adaptive fuzzy clustering (IAFC) with Bayesian decision theory, and finally derives agglomerative IBFC based on its Bayesian interpretation. IAFC performs a vigilance test so that outliers can be eliminated from learning procedure. However, we have no theoretical background on the rationality of the test. Thus, we claim that the decision and vigilance test of IBFC follow Bayesian minimum risk classification rule within a framework of Bayesian decision theory. Moreover, based on this interpretation, we propose Agglomerative IBFC capable of clustering data of complex structure. Test on synthetic data shows an outstanding success rate, and test on benchmark data shows that our proposed method performs better than several existing methods.