New Cluster Detection Based on Multi-Representation Index Tree Text Clustering

Abstract

Traditional Clustering is a powerful technique for revealing the "hot" topics among documents. However, it's hard to discover the new type events coming out gradually. In this paper, we propose a novel model for detecting new clusters from time-streaming documents. It consists of three parts: the cluster definition based on Multi-Representation Index Tree (MI-Tree), the new cluster detecting process and the metrics for measuring a new cluster. Compared with the traditional method, we process the newly coming data first and merge the old clustering tree into the new one. This algorithm can avoid this effect: the documents enjoying high similarity were assigned to different clusters. We designed and implemented a system for practical application, the experimental results on a variety of domains demonstrate that our algorithm can recognize new valuable clusters during the iteration process, and produce quality clusters.