A On-Line News Documents Clustering Method

摘要

To improve the efficiency and accuracy of on-line news event detection (ONED) method, we select the words that their term frequency (TF) is greater than a threshold to create the vector space model of the news document, and propose a two-stage clustering method for ONED. This method divides the detection process into two stages. In the first stage, the similar documents collected in a certain period of time are clustered into micro-clusters. In the second stage, the micro-clusters are compared with previous event clusters. The experimental results show that the proposed method has fewer computation load, higher computing rate, and less loss of accuracy.