中文摘要：

针对由领域本体匹配产生的文本特征矩阵,描述了一种基于改进相似度计算公式的文本资料聚类算法。实验证明,当我们以生物医药领域的文本作为实验样本时,不管是从熵值还是从纯度来考虑,基于领域本体改进的聚类算法都要优于K-means算法和凝聚层次聚类算法。

英文摘要：

This paper describes a new clustering method for texts based on an improved similarity calcula tion formula for text feature matrix which is generated by domain ontology matching.The experiment shows that： when they use texts in the field of bio-medicine as the experimental samples,the new cluster ing method for texts based on an improved similarity calculation formula is better than the K-means clus tering method and agglomerative hierarchical clustering method from entropy and purity considerations.