A New Method of Hierarchical Text Clustering Based on Lsa-Hgsom

Jianfeng Wang, Lina Ma, Xinye Li, Yangxiu Zhou, Dong Qiao

Abstract


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 LSA-HGSOM 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.


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Modern Applied Science   ISSN 1913-1844 (Print)   ISSN 1913-1852 (Online)

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