Study of subspace clustering algorithm of high dimensional data based on variable weighting methods
Yang Shuang · Informationization · 2009
The sparsity and the problem of the curse of dimensionality of high-dimensional data, make the most of traditional clustering algorithms lose their action in high-dimensional space. Therefore, clustering of data in a high-dimensional space becomes a hot research area. Subspace clustering algorithm is one of the effective ways to handle problems of high-dimensional data clustering. This paper introduces and realizes two algorithms (SCAD and EWKM) that discover clusters in subspaces spanned by different combinations of dimensions via local weightings of features. We experiment these algorithms using synthetic datasets and real datasets, then analyze the results and contrast their performance and applicable occasions.