一种基于族相合性的文本增量聚类箅法

传统文本聚类方法只适合处理静态样本，且时间复杂度较高。针对该问题，提出一种基于簇相合性的文本增量聚类算法。采用基于词项语义相似度的文本表示模型，利用词项之间的语义信息，通过计算新增文本与已有簇之间的相合性实现对文本的增量聚类。增量处理完部分文本后，对其中错分可能性较大的文本重新指派类别，以进一步提高聚类性能。该算法可在对象数据不断增长或更新的情况下，避免大量重复计算，提高聚类性能。在20 Newsgroups数据集上进行实验，结果表明，与k-means算法和SHC算法相比，该算法可减少聚类时间，提高聚类性能。

An Incremental Text Clustering Algorithm Based on Cluster Congruence

Traditional text clustering methods are only suitable for static sample, and their time complexity is too high. Aiming at these problems, this paper proposes a new Incremental Text Clustering Algorithm Based on Congruence（ITCAC） between text and cluster. The new algorithm can avoid a lot of double counting to improve the performance of clustering. It uses text representation model based on semantic similarity of lexical items, fully takes the semantic information between terms into account and computes the congruence between new documents and existing clusters. After processing part of the documents, the algorithm reassigns the categorization of documents that has large possibility of misclassification to further improve the clustering performance. Experimental results on 20 Newsgronps datasets show that, compared with the k-means algorithm and SHC algorithm, the new algorithm not only has less clustering time, but also has better performance of clustering.