A Novel Parameter-Light Subspace Clustering Technique Based on Single Linkage Method

Abstract

Subspace clustering is a challenging high-dimensional data mining task. There have been several approaches proposed in the literature to identify clusters in subspaces, however their performance and quality is highly affected by input parameters. A little research is done so far on identifying proper parameter values automatically. Other observed drawbacks are requirement of multiple database scans resulting into increased demand for computing resources and generation of many redundant clusters. Here, we propose a parameter light subspace clustering method for numerical data hereafter referred to as CLUSLINK. The algorithm is based on single linkage clustering method and works in bottom up, greedy fashion. The only input user has to provide is how coarse or fine the resulting clusters should be, and if not given, the algorithm operates with default values. The empirical results obtained over synthetic and real benchmark datasets show significant improvement in terms of accuracy and execution time.

An Efficient Density Parameter-Light in Enhanced Subspace Clustering in High Dimensional Data

“…They adjust Expectation Maximisation (EM) algorithm to determine on states of the parameters needed by the PMoG-LRR model. A Novel Parameter-Light Subspace Clustering Technique [24], is a subspace clustering method follows Single Linkage Method for numerical data called as CLUSLINK. The algorithm is following a single linkage clustering method and works in a bottom-up, greedy manner.…”