ART-C: a neural architecture for self-organization under constraints

Proposes an ART-based neural architecture known as ART-C (ART under constraints) that performs online clustering of pattern sequences subject to the constraints on the recognition category representation. Experiments on two real-life data sets show that ART-C produces reasonably good clustering qualities, with the added advantage of high efficiency.

[1]  Michalis Vazirgiannis,et al.  Quality Scheme Assessment in the Clustering Process , 2000, PKDD.

[2]  Yiming Yang,et al.  A re-examination of text categorization methods , 1999, SIGIR '99.

[3]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[4]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[5]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[6]  Issam Dagher,et al.  Properties of learning of a Fuzzy ART Variant , 1999, Neural Networks.

[7]  José Carlos Príncipe,et al.  A new clustering evaluation function using Renyi's information potential , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[8]  Yiming Yang,et al.  A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.

[9]  Ah-Hwee Tan,et al.  Adaptive resonance associative map , 1995, Neural Networks.

[10]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .

[11]  R. White,et al.  Incremental Learning and Optimization of Hierarchical Clusterings with Art-Based Modular Networks , 2000 .

[12]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[13]  Arthur Flexer,et al.  On the use of self-organizing maps for clustering and visualization , 1999, Intell. Data Anal..

[14]  Palma Blonda,et al.  A survey of fuzzy clustering algorithms for pattern recognition. I , 1999, IEEE Trans. Syst. Man Cybern. Part B.