SCAF An effective approach to Classify Subspace Clustering algorithms

TL;DR

This paper introduces SCAF, a classification scheme for subspace clustering algorithms based on characteristics like cluster orientation and dimension overlap, aiding better understanding and comparison of these algorithms.

Contribution

The paper proposes the SCAF framework, a systematic classification scheme for subspace clustering algorithms based on their characteristics.

Findings

SCAF categorizes subspace clustering algorithms effectively.

Provides a systematic comparison of algorithms within a SCAF family.

Facilitates better understanding and selection of algorithms for specific tasks.

Abstract

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.