Fast growing self organizing map for text clustering

Authors:
Sumith Matharage;Damminda Alahakoon;Jayantha Rajapakse;Pin Huang
Affiliations:
Clayton School of Information Technology, Monash University, Australia;Clayton School of Information Technology, Monash University, Australia;School of Information Technology, Monash Univeristy, Malaysia;Clayton School of Information Technology, Monash University, Australia
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Year:
2011

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Abstract

This paper presents an integration of a novel document vector representation technique and a novel Growing Self Organizing Process. In this new approach, documents are represented as a low dimensional vector, which is composed of the indices and weights derived from the keywords of the document. An index based similarity calculation method is employed on this low dimensional feature space and the growing self organizing process is modified to comply with the new feature representation model. The initial experiments show that this novel integration outperforms the state-of-the-art Self Organizing Map based techniques of text clustering in terms of its efficiency while preserving the same accuracy level.