A Modified Fuzzy ART for Soft Document Clustering

Author(s): Kondadadi, R. | Kozma, R. |

Year: 2002

Citation: International Joint Conference on Neural Networks, World Congress on Computational Intelligence, Honolulu, Hawaii, pp. 2545-2549

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.

Topics: Machine Learning, Applications: Information Fusion, Models: ART 2 / Fuzzy ART, Modified ART,

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