Projective Clustering Method for the Detection of Outliers in Non-Axis Aligned Subspaces

Clustering the case of non-axis-aligned subspaces and detection of outliers is a major challenge due to the curse of dimensionality. The normal clustering was efficient in axis-aligned subspaces only. 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. A projective clustering is proposed for outlier detection in high dimensional dataset that discovers the detection of possible outliers and non-axis - aligned subspaces in a data set and to build a robust initial condition for the clustering algorithm. Fuzzy Logic is mainly used to find the empty space.