A New Topic Detection and Tracking Approach Combining Periodic Classification and Single-Pass Clustering
For the insufficient model and accuracy of incremental cluster topic, the problems of miss alarm and false alarm may be increased due to the accumulate effects. The topic detection and tracking method of periodic classification and signle-pass cluster was proposed in this paper, the main ideal is to employ the incremental clustering algorithm to detect and track topic, When the every news text accumulate to a certain degree, the clustering reports were cycle classifyed to improve the accuracy of topic clusters, and follow-up to improve the accuracy of topic detection and tracking. The experiment results shown the effectivity of the method, which could decrease the probabilities of miss alarm and false alarm, then finally reducing the normalized detection cost.
topic detection and trackingincremental clusteringtext categorizationk-nearest neighbor classifier
国家科技期刊平台
NSTL
万方数据
维普
