Improving performance of similarity-based clustering by feature weight learning

Similarity-based clustering is a simple but powerful technique which usually results in a clustering graph for a partitioning of threshold values in the unit interval. The guiding principle of similarity-based clustering is similar objects are grouped in the same cluster. To judge whether two objects are similar, a similarity measure must be given in advance. The similarity measure presented in this paper is determined in terms of the weighted distance between the features of the objects. Thus, the clustering graph and its performance (which is described by several evaluation indices defined in this paper) will depend on the feature weights. This paper shows that, by using gradient descent technique to learn the feature weights, the clustering performance can be significantly improved. It is also shown that our method helps to reduce the uncertainty (fuzziness and nonspecificity) of the similarity matrix. This enhances the quality of the similarity-based decision making.