Automatic Generation of Fuzzy Membership Functions using Adaptive Mean-shift and Robust Statistics.

In this paper, an unsupervised approach incorporating variable bandwidth mean-shift and robust statistics is presented for generating fuzzy membership functions from data. The approach takes an attribute and automatically learns the number of representative functions from the underlying data distribution. Given a specific membership function, the approach also works out the associated parameters. The investigation here examines the application of approach using the triangular membership function. Results from partitioning of attributes confirm that the generated membership functions can better separate the underlying distributions when compared to a number of other techniques. Classification performance of fuzzy rule sets produced using four different methods of parameterizing the associated attributes is examined. We observed that the classifier constructed using the proposed method of generating membership function outperformed the 3 other classifiers that had used other methods of parameterizing the attributes.