Impact Parameter Analysis of Subspace Clustering

Subspace clustering, which detects all clusters in affine subspaces of a given high dimensional vector space, is used in various applications, including e-business. The performance and result of a subspace clustering algorithm highly depend on the parameter values the algorithm is tuned to execute. It may not be clear if the resultant clusters are indeed meaningful ones in a given dataset or if the result is just an artifact of the given parameter values. Although choosing the proper parameter values is crucial for both clustering quality and performance of the algorithm, there has been little research or discussion on this topic. In this paper, we propose a methodology for determining proper values of parameters in subspace clustering. Along with it, we validate our approach through experimental analysis, using various real-world datasets. The study can serve as a reference model for any subspace clustering experiment in which parameter setting is required to output clusters of quality.