  Subspace clustering discovers the clusters embedded in multiple, overlapping
subspaces of high dimensional data. Many significant subspace clustering
algorithms exist, each having different characteristics caused by the use of
different techniques, assumptions, heuristics used etc. A comprehensive
classification scheme is essential which will consider all such characteristics
to divide subspace clustering approaches in various families. The algorithms
belonging to same family will satisfy common characteristics. Such a
categorization will help future developers to better understand the quality
criteria to be used and similar algorithms to be used to compare results with
their proposed clustering algorithms. In this paper, we first proposed the
concept of SCAF (Subspace Clustering Algorithms Family). Characteristics of
SCAF will be based on the classes such as cluster orientation, overlap of
dimensions etc. As an illustration, we further provided a comprehensive,
systematic description and comparison of few significant algorithms belonging
to 'Axis parallel, overlapping, density based' SCAF.
