A Evolutionary Fuzzy ART Computation for the Document Clustering
Abstract
Many clustering techniques have been widely
developed in order to retrieve, filter, and categorize documents available in the database or even on the Web. The issue to appropriately organize and store the information in terms of documents clustering becomes very crucial for the purpose of knowledge discovery and management. In this work, a hybrid intelligent approach has been proposed to automate the clustering process based on the characteristics of each document represented by the fuzzy concept networks. Through the proposed approach, the useful knowledge can be clustered and then utilized effectively and
efficiently. In literature, artificial neural network have been widely applied for the document-clustering applications. However, the number of documents is huge so that it is hard to find the most appropriate ANN parameters in order to get the most appropriate clustering results. Traditionally, these parameters are adjusted manually by the way of trial and error so that it is time consuming and doesn’t guarantee an optimum result. Therefore, a hybrid approach incorporating an evolutionary computation (EC) approach and a Fuzzy Adaptive Resonance Theory (Fuzzy-ART) neural network has been proposed to adjust the Fuzzy-ART parameters
automatically.
Keywords
Full Text:
PDFReferences
Bhandarkar, S.M., Zhang, Y., and Potter, W.E.(1994), “An edge
detection technique using genetic algorithm-based optimization,” Pattern
Recognition, 27(9), 1159-1180.
Carpenter, G., and Grossberg, S., (1987), “A massively parallel
architecture for a self-organizing neural patter recognition machine,
”Computer Vision, Graphics, and Image Processing, 37, pp. 54-115.
Carpenter, G., and Grossberg, S. (1991), “Fuzzy ART: Fast stable
learning and categorization of analog patterns by an adaptive resonance
system,” NN, 4, pp. 759-771.
Chen, Shyi-Ming and Horng, Yih-Jen (1999), “Fuzzy Query Processing
for Document Retrieval Based on Extended Fuzzy Concept Networks,”
IEEE Transactions on Systems, Man, and Cybernetics-Part B: 29(1),
pp.96-104.
Chen, T.C. (1997), “A Hybrid Intelligent System for Process Modeling
and Control Using a Neural Network and a Genetic Algorithm,” Ph.D.
Thesis in the University of Iowa, Iowa
Hsieh, Su, Liaw “Fuzzy ART for the Document Clustering By Using
Evolutionary Computation”, WSEAS Transactions on Computers
(2010),pp.1032-1041
Chen, T. C. and You, P. S. (2000), “An efficient evolutionary
computation approach for the vending machine inventory control
problem,” Journal of the Chinese Institute of Industrial Engineers, 17(4),
pp. 451-457.
Fogel, D. D. (1994), “An Introduction to simulated evolutionary
optimization,” IEEE Transactions on Neural Networks, 5(1), pp. 3- 14.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.