A COMPARISON OF K-MEANS AND FUZZY C-MEANS USING BACKGROUND KNOWLEDGE

Relevant Component Analysis has been introduced recently as a way to incorporate a priori information, such as class or preference information, that may exist for a given data set. The method uses this information to define a new Mahalanobis distance metric on the data space. The purpose of the present paper is to investigate the difference, if any, that Relevant Component Analysis makes when applied in conjunction with the clustering algorithms of k-means or fuzzy c-means. Results are given for six standard data sets.