A modified fuzzy art for soft document clustering (2002)
| Venue: | In: Proc. International Joint Conference on Neural Networks |
| Citations: | 9 - 2 self |
BibTeX
@INPROCEEDINGS{Kondadadi02amodified,
author = {Ravikumar Kondadadi and Robert Kozma},
title = {A modified fuzzy art for soft document clustering},
booktitle = {In: Proc. International Joint Conference on Neural Networks},
year = {2002},
pages = {2545--2549},
publisher = {IEEE}
}
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Abstract
Document clustering is a very useful application in recent days especially with the advent of the World Wide Web. Most of the existing document clustering algorithms either produce clusters of poor quality or are highly computationally expensive. In this paper we propose a document-clustering algorithm, KMART, that uses an unsupervised Fuzzy Adaptive Resonance Theory (Fuzzy-ART) neural network. A modified version of the Fuzzy ART is used to enable a document to be in multiple clusters. The number of clusters is determined dynamically. Some experiments are reported to compare the efficiency and execution time of our algorithm with other document-clustering algorithm like Fuzzy c Means. The results show that KMART is both effective and efficient. 1.
