A Novel Dencos Model For High Dimensional Data Using Genetic Algorithms

Abstract

Subspace clustering is an emerging task that aims at detecting clusters in entrenched in subspaces. Recent approaches fail to reduce results to relevant subspace clusters. Their results are typically highly redundant and lack fact of considering critical the density divergence problem, in discovering clusters, where they utilize an absolute density value as density threshold to identify dense regions in all subspaces. Considering varying region densities in different subspace cardinalities, we note that a more appropriate way to determine whether a region in a subspace should be identified as dense is by comparing its density with region densities in that subspace. Based on this idea and due to infeasibility of applying previous techniques in this novel clustering model, we devise an innovative algorithm, referred to as DENCOS(DENsity Conscious Subspace clustering), to adopt a divide-and-conquer scheme to efficiently discover clusters satisfying different density thresholds in different subspace cardinalities. DENCOS can discover clusters in all subspaces with high quality, and efficiency significantly outperforms previous works, thus demonstrating its practicability for subspace clustering. As validated by our extensive experiments on retail dataset, it outperforms previous works. We extend our work with a clustering technique based on genetic algorithms which is capable of optimizing number of clusters for tasks with well formed and separated clusters.