Abstract
Clustering is generally done on individual object data representing the entities such as feature vectors or on object relational data incorporated in a proximity matrix.This paper describes another method for finding a fuzzy membership matrix that provides cluster membership values for all the objects based strictly on the proximity matrix. This is a form of relational data clustering. The fuzzy membership matrix is found by first finding a set of vectors that approximately have the same Euclidian distances as the proximities that are provided. These vectors can be of very low dimension. Fuzzy c-means (FCM) is then applied to these vectors to obtain the fuzzy membership matrix. In addition two-dimensional vectors are created to allow a visual representation of the proximity matrix. This allows comparison of the result of automatic clustering with visual clustering. The method proposed here is compared to other relational clustering methods using various proximity matrices as input. Simulations show the method to be very effective.
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