Independent Component Analysis for Image Recovery Using SOM-Based Noise Detection

Zhang, Xiaowei; Lü, Jianming; Yahagi, Takashi

doi:10.1093/ietfec/e90-a.6.1125

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2009

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“…Such types of topological structures would also denote the characteristic of the input samples. SOM has been employed in a wide range of applications in various different domains, including speech recognition, image data compression, robot control, pattern recognition, and medical diagnosis [13], [14], [17], [18], [20], [22], [24], [26], [27].…”

Section: Introductionmentioning

confidence: 99%

A New Clustering Validity Index for Cluster Analysis Based on a Two-Level SOM

Shieh

Liao

2009

IEICE Trans. Inf. & Syst.

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SUMMARYSelf-Organizing Map (SOM) is a powerful tool for the exploratory of clustering methods. Clustering is the most important task in unsupervised learning and clustering validity is a major issue in cluster analysis. In this paper, a new clustering validity index is proposed to generate the clustering result of a two-level SOM. This is performed by using the separation rate of inter-cluster, the relative density of inter-cluster, and the cohesion rate of intra-cluster. The clustering validity index is proposed to find the optimal numbers of clusters and determine which two neighboring clusters can be merged in a hierarchical clustering of a two-level SOM. Experiments show that, the proposed algorithm is able to cluster data more accurately than the classical clustering algorithms which is based on a two-level SOM and is better able to find an optimal number of clusters by maximizing the clustering validity index.

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