关键词: ART2神经网络, 最近邻, 隐含语义分析（LSA）, 降维, 文本聚类, 聚类分析

Abstract: In order to realize dynamic clustering for high-dimensional text data, an improved ART2 neural network text clustering algorithm based on Latent Semantic Analysis（LSA） is proposed, which emerges the semantic relations between texts and terms and reduces the noises, the dimensionality and the computation complexity by LSA. The new algorithm uses an improved intermediate learning method to simplify calculating procedures and accelerate the computation of the ART2 network, and uses the nearest neighbor reformation to improve the stability and weaken the dependence of samples order for the ART2 network clustering. Experiments demonstrate that this improved algorithm can realize dynamic text clustering effectively.

Key words: ART2 neural network, nearest neighbor, Latent Semantic Analysis（LSA）, dimensionality reduction, text clustering, clustering analysis