An Optimized Hybrid Kohonen Neural Network for Ambiguity Detection in Cluster Analysis Using Simulated Annealing

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Abstract

One of the popular tools in the exploratory phase of Data mining and Pattern Recognition is the Kohonen Self Organizing Map (SOM). The SOM maps the input space into a 2-dimensional grid and forms clusters. Recently experiments represented that to catch the ambiguity involved in cluster analysis, it is not necessary to have crisp boundaries in some clustering operations. In this paper to overcome the ambiguity involved in cluster analysis, a combination of Rough set Theory and Simulated Annealing is proposed that has been applied on the output grid of SOM. Experiments show that the proposed two-stage algorithm, first using SOM to produce the prototypes then applying rough set and SA in the second stage in order to assign the overlapped data to true clusters they belong to, outperforms the proposed crisp clustering algorithms (i.e. I-SOM) and reduces the errors.