Using latent semantic analysis to improve access to textual information
CHI '88 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Data Mining and Knowledge Discovery
DEXA '99 Proceedings of the 10th International Conference on Database and Expert Systems Applications
Dynamic self-organizing maps with controlled growth for knowledge discovery
IEEE Transactions on Neural Networks
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Text clustering has been recognized as an important component in data mining. Self-Organizing Map (SOM) based models have been found to have certain advantages for clustering sizeable text data. However, current existing approaches lack in providing an adaptive hierarchical structure within in a single model. This paper presents a new method of hierarchical text clustering based on combination of latent semantic analysis (LSA) and hierarchical GSOM, which is called LSA-HGSOM method. The text clustering result using traditional methods can not show hierarchical structure. However, the hierarchical structure is very important in text clustering. The LSAHGSOM method can automatically achieve hierarchical text clustering, and establishes vector space model (VSM) of term weight by using the theory of LSA, then semantic relation is included in the vector space model. Both theory analysis and experimental results confirm that LSA-HGSOM method decreases the number of vector, and enhances the efficiency and precision of text clustering.