An adaptive integrated fuzzy clustering model for pattern recognition

The extension of neural net based crisp clustering algorithms to fuzzy clustering algorithms has been addressed by many researchers in recent years. However, such neuro-fuzzy clustering algorithms developed so far suffer from restrictions in identifying the actual decision boundaries among clusters with overlapping regions. These restrictions are induced by the choice of the similarity measure and representation of the clusters. An integrated adaptive fuzzy clustering (IAFC) algorithm is presented to generate improved decision boundaries by introducing a new similarity measure and by integrating the advantages of the fuzzy optimization constraint of fuzzy c-means (FCM), the control structure of adaptive resonance theory (ART-1), and a fuzzified Kohonen-type learning rule. The effect of the new similarity measure in finding nonlinear decision boundaries among closely located cluster centroids is demonstrated with computer generated data. We use the IRIS data set and a subset of the tethered satellite system simulation data set to compare the convergence rate and misclassifications resulting from IAFC algorithm with other clustering algorithms.