Analysis of Unsupervised Clustering by Crossing Minimization

Citation

Abdullah A & Hussain A (2004) Analysis of Unsupervised Clustering by Crossing Minimization. In: Smith L, Hussain A & Aleksander I (eds.) Brain Inspired Cognitive Systems 2004, 29 August - 1 September 2004, University of Stirling, Scotland, UK. Brain Inspired Cognitive Systems - BICS2004, Stirling, UK, 29.08.2004-01.09.2004. Stirling, Scotland: University of Stirling, pp. 27-28. http://www.cs.stir.ac.uk/~lss/BICS2004/CD/bookofabstracts.pdf

Abstract
It was demonstrated for the first time that crossing minimization of bipartite graphs can be used to perform unsupervised clustering. In this paper, we present a detailed analysis of the bipartite graph model used to perform unsupervised clustering. We also discuss the effect of data discretization, followed by simulation results which demonstrate the noise immunity of the proposed technique. Finally we use the proposed technique to discover useful hidden patterns in real agriculture data.