Random neighbourhood dynamic clustering Online publication date: Fri, 28-Sep-2018
by Nilesh Patel; Gaurav Tyagi; Pawel Marcinek
International Journal of Computational Vision and Robotics (IJCVR), Vol. 8, No. 5, 2018
Abstract: Recognition of arbitrary shaped clusters is a highly active research topic in data mining and cluster analysis. In this paper, we consider the problem of data clustering of arbitrary shaped clusters as a random evolutionary process. We propose a new algorithm RNDC which uses the random process for cluster analysis. RNDC assumes that an object contains information only about the characteristic values of its local neighbourhood. It explores the local cluster structures to determine the global partitions of dataset. It is of significance that RNDC evolve randomly among the objects of dataset, while other well-known partitioning clustering algorithms used the techniques of sequential propagation through the nearest connected/reachable objects. Our method is, in principle, applicable for any arbitrary shaped clusters. Since randomness is an essential part of RNDC, it makes this algorithm suitable for multiprocessing parallel computation.
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