Quality Evaluation for Three Textual Document Clustering Algorithms
文本聚类算法的质量评价

Textual document clustering is one of the effective approaches to establish a classification instance of a huge textual document set. Clustering Validation or Quality Evaluation techniques can be used to assess the efficiency and effectiveness of a clustering algorithm. This paper presents the quality evaluation criterions. Based on these criterions we take three typical textual document clustering algorithms for assessment with experiments. The comparison results show that STC(Suffix Tree Clustering) algorithm is better than k-Means and Ant-Based clustering algorithms. The better performance of STC algorithm comes from that it takes into account the linguistic property when processing the documents. Ant-Based clustering algorithm's performance variation is affected by the input variables. It is necessary to adopt linguistic properties to improve the Ant-Based text clustering's performance.