Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/38155
標題: A new approach for data clustering and visualization using self-organizing maps
作者: Shieh, Shu-Ling
Liao, I-En
關鍵字: Self-organizing map
Unsupervised learning
Visualization
Clustering method
摘要: A self-organizing map (SOM) is a nonlinear, unsupervised neural network model that could be used for applications of data clustering and visualization. One of the major shortcomings of the SOM algorithm is the difficulty for non-expert users to interpret the information involved in a trained SOM. In this paper, this problem is tackled by introducing an enhanced version of the proposed visualization method which consists of three major steps: (1) calculating single-linkage inter-neuron distance, (2) calculating the number of data points in each neuron, and (3) finding cluster boundary. The experimental results show that the proposed approach has the strong ability to demonstrate the data distribution, inter-neuron distances, and cluster boundary, effectively. The experimental results indicate that the effects of visualization of the proposed algorithm are better than that of other visualization methods. Furthermore, our proposed visualization scheme is not only intuitively easy understanding of the clustering results, but also having good visualization effects on unlabeled data sets.
URI: http://hdl.handle.net/11455/38155
ISSN: 0957-4174
文章連結: http://dx.doi.org/10.1016/j.eswa.2012.02.181
Appears in Collections:資訊科學與工程學系所

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