关键词: 文本聚类, 向量空间模型, 传统的且非常高效的层次聚类算法（BIRCH）, K最近邻

Abstract: In order to improve the effect of text clustering, and to mend the flaws of traditional clustering algorithm in parameter setting and algorithm stability, a new text clustering algorithm TCBIBK（a Text Clustering algorithm Based on Improved BIRCH and K-nearest neighbor） is presented. TCBIBK uses BIRCH clustering algorithm as the prototype. During the process of clustering, besides analyzing the distance between text objects and clusters, TCBIBK also analyzes the distance between clusters and clusters, takes the active cluster merging or segmentation, and sets the dynamic threshold. Combined with KNN classification algorithm, TCBIBK improves the algorithm stability under the premise of ensuring the good efficiency of clustering. When applied to text clustering, TCBIBK can improve the text clustering effect. The results of comparative experiment shows that this algorithm can greatly improve the validity and stability of text clustering.

Key words: text clustering, vector space model, Balanced Iterative Reducing and Clustering using Hierarchies（BIRCH）, K-nearest neighbor