A K-means-based Algorithm for Projective Clustering

In this paper, a new algorithm for projective clustering is proposed. The algorithm consists of two phases. The first phase performs attribute relevance analysis by detecting dense regions in each attribute, thereby allowing irrelevant attributes and outliers to be captured and eliminated. Starting from the results of the first phase, the second phase aims to uncover clusters in different subspaces. The clustering process is based on the k-means algorithm, with the computation of distance restricted to subsets of attributes where object values are dense