| Summary: | 碩士 === 國立成功大學 === 資訊管理研究所 === 95 === In the forthcoming era of knowledge economics, the amount of information has been growing exponentially. To the challenge of information overloading, the self-organizing map (SOM) artificial neural network is an unsupervised learning method which has been widely used for clustering. It is capable of mapping high-dimensional data onto a low-dimensional grid such that similar data elements are placed close together. A large size of SOM has the advantage of smaller internal cluster dispersion than a small size of SOM. Furthermore, after SOM uses agglomerative clustering method, the clustering number of individual user demand can be presented. However, the agglomerative clustering method used on SOM is not the only one. Therefore, a central issue of research in the area is of SOM used on agglomerative clustering method.
In the paper we present a comparison between two Agglomerative clustering methods including TreeSOM and Two-Layer SOM, which is a special case of Multilayer SOM. Simultaneously, this study aims to address this problem by estimating their agglomerative clustering preferences. In data collection, we employed real-world data and artificial data generated using Monte Carlo simulation, which were simulated considering correlated and uncorrelated variables, non-overlapping and overlapping clusters with and without outliers.
The results showed that TreeSOM had a very good performance in most of the cases. On the other hand, Two-Layer SOM did not perform well in almost any cases because it was very affected by the number of variables and clusters. For the most part, the average performances of TreeSOM were better than those of Two-Layer SOM, and the differences were statistically significant.
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