A Novel Burst-based Text Representation Model for Scalable Event Detection
Abstract— A Novel Burst-based Text Representation Model for Scalable Event Detection. 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. < Final Year Projects > 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.
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