New Trending Events Detection based on the Multi-Representation Index Tree Clustering

Traditional Clustering is a powerful technique for revealing the hot topics among Web information. However, it failed to discover the trending events coming out gradually. In this paper, we propose a novel method to address this problem which is modeled as detecting the new cluster from time-streaming documents. Our approach concludes 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. Our algorithm can avoid that the documents owning 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 cluster during the iteration process, and produce quality clusters.