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Building an Adaptive Hierarchy of Clusters for Text Data

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3 Author(s)
Chen, S. ; Fac. of Inf. Technol., Monash Univ., Clayton, Vic. ; Alahakoon, D. ; Indrawan, M.

Text clustering has been recognized as an important component in Web-based applications. Clustering data on a hierarchical structure enables exploring data on different levels of granularity, providing a more intuitive view that is close to the way humans view the world. 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 proposes an unsupervised hierarchical clustering approach based on the growing self-organizing map (GSOM). By utilizing GSOM's spread factor, our approach offers an adaptive architecture with the capability of detecting necessary layers to form a hierarchy, avoiding a number of issues that a traditional top-down or bottom-up hierarchical clustering approach often encounter. Experiment has shown that this approach has the potential for efficiently clustering heterogeneous text data

Published in:

Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on  (Volume:2 )

Date of Conference:

28-30 Nov. 2005

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