Unsupervised learning network based on gradient descent procedure of fuzzy objective function

Fuzzy clustering has been playing an important role in solving many problems. Fuzzy c-means (FCM) algorithm is most frequently used for fuzzy clustering. But some fixed points of FCM algorithm, known as Tucker´s counter example, is not a reasonable solution. Moreover, the FCM algorithm is impossible to perform online learning since it is basically a batch learning scheme. This paper presents an unsupervised learning network as an attempt to improve the shortcomings of conventional clustering algorithms. This model integrates the optimization function of FCM algorithm into an unsupervised learning network. The learning rule of the proposed scheme is a result of formal derivation based on the gradient descent procedure of fuzzy objective function. Using the result of formal derivation, two implementations of the proposed scheme, the batch learning version and online learning version, are devised. They are tested on Chiou´s data and Iris data and compared with FCM. Experimental results show that the proposed scheme derived the reasonable solution on Tucker´s counter example