Using a neuro-fuzzy technique to improve the clustering based on similarity

Although there have been many approaches to fuzzy clustering, the clustering based on a similarity matrix is still a popular technique which performs by means of transforming the similarity matrix into its transitive closure. The clustering performance depends strongly on the similarity matrix in which elements are determined according to a distance metric in many situations. For a given case library in which diverse similarity measures can be defined, different similarity matrixes result in different clustering results. This paper introduces the concept of feature weight and then incorporates this concept into the profess of computing similarity between two cases, such that the similarity matrix relies on these feature weights. The purpose of this paper is to improve the clustering performance by adjusting these weights in terms of a neural-fuzzy technique. To learn the feature weights, a neural network is designed for minimizing an objective function. For achieving a local minimum of the objective function, the gradient-descent technique is used to train this network. Several indexes for measuring the quality of a clustering result are defined in this paper to compare the performance.