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

Bhagyashri A. Kelkar (), Sunil F. Rodd and Umakant P. Kulkarni
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Bhagyashri A. Kelkar: Department of CSE, Sanjay Ghodawat University, Atigre Kolhapur 416118, India
Sunil F. Rodd: Department of CSE, Gogte Institute of Technology, Belagavi, Karnataka 590008, India
Umakant P. Kulkarni: Department of CSE, SDMCET Dharwar, Karnataka 580002, India

Journal of Information & Knowledge Management (JIKM), 2019, vol. 18, issue 01, 1-23

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.

Keywords: Subspace clustering; single linkage clustering; high-dimensional data; parameter estimation (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S0219649219500072

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