[IC] Clustering of Trained Self-Organizing Feature Maps based on s-t Graph Cuts

Abstract.

The Self-organizing Feature Map(SOFM) that is one of unsupervised neural networks is a very powerful tool for data clustering and visualization in high-dimensional data sets. Although the SOFM has been applied in many engineering problems, it needs to cluster similar weights into one class on the trained SOFM as a post-processing, which is manually performed in many cases. The traditional clustering algorithms, such as k-means, on the trained SOFM, but do not yield satisfactory results, especially when clusters have arbitrary shapes. This paper proposes automatic clustering on trained SOFM via graph cuts, which can both deal with arbitrary cluster shapes and be globally optimized by graph cuts. When using graph cuts, the graph must have two additional nodes, called terminals, and weights between the terminals and nodes of the graph are generally setting based on data manually obtained by users. The proposed method automatically sets the weights based on mode-seeking on a distance matrix. Experimental results demonstrated the effectiveness of the proposed method in texture segmentation. In the experimental results, the proposed method improved precision rates compared with previous traditional clustering algorithm, as the method can deal with arbitrary cluster shapes based on the graph-theoretic clustering and globally optimize the clustering of the trained SOFM by graph cuts.

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2007.