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Clustering of gene expression patterns is of great value for the understanding of the various molecular biological processes. While a number of algorithms have been applied to gene clustering, there are relatively few studies on the application of neural networks to this task. In addition, there is a lack of quantitative evaluation of the gene clustering results. This paper proposes Adaptive Resonance Theory under Constraint (ART-C) for efficient clustering of gene expression data. We illustrate that ART-C can effectively identify gene functional groupings through a case study on rat CNS data. Based on a set of quantitative evaluation measures, we compare the performance of ART-C with those of K-Means, SOM, and conventional ART. Our comparative studies on the yeast cell cycle and the human hematopoietic differentiation data sets show that ART-C produces reasonably good quantitative performance. More importantly, compared with K-Means and SOM, ART-C shows a significantly higher learning efficiency, which is crucial for knowledge discovery from large scale biological databases.