Effective utilization of dataspace with projective clustering

Clustering high-dimensional data is a major challenge due to the curse of dimensionality. To solve this problem, projective clustering has been defined as an extension to traditional clustering that attempts to find projected clusters in subsets of the dimensions of a data space. Then, a model-based algorithm for fuzzy projective clustering that discovers clusters with overlapping boundaries in various projected subspaces will discuss. Fuzzy Logic is mainly used to find the empty space. In model-based methods, data are thought of as originating from various possible sources, which are typically modelled by Gaussian mixture.