A System for Event Detection and Tracking Based on Constructive-Competition Clustering and KNNFL
一种基于构建2竞争聚类及KNNFL 的事件探测与追踪系统

The objective of event detection and tracking is to automatically spot previously unreported new events from news-feed and assign documents to previously spotted events.A Constructive-Competition Clustering(C3) method was used for topic relevant event detection in this paper,which is motivated by constructive and competitive learning from neural network research.In addition,a classification method based on K Nearest Neighbor Feature Line(KNNFL) was proposed for tracking events,this method based on Nearest Feature Line(NFL) is essentially an extension of the K Nearest Neighbor(KNN) method,NFL combining with improved KNN produces KNNFL in order to make it more suitable to news event analyzing.The study indicates that the proposed methods in this paper achieve superior performance than the traditional incremental k-means,Single-Pass clustering,Rocchio and KNN.The computational analysis has showed that,KNNFL behaves robustly even if the number of positive training examples is extremely small.