A Method for Discovering and Obtaining Company Hot Events from Internet News

With the rapid development and popularization, Internet is becoming the most convenient way to publish and obtain information, which causes an extremely increasing quantity and variety of data. It is difficult to find out potentially valuable information from these data, which is the primary problem of data mining. Mining company hot events from Internet news can effectively reflect how its business works. Thus, we propose a method for discovering and obtaining hot events from Internet news. In the proposed method, we use Gaussian kernel to update clustering center instead of global cluster to modify Single-Pass clustering algorithm. It is a dynamic incremental clustering algorithm which does not need to initialize the number of clusters. Then, Top-N hot events can be obtained through the clustering centers. Experimental comparison shows that the improved algorithm has higher clustering efficiency than the classic algorithm. Case studies from Shanghai pilot free-trade zone (FTZ) also show the effectiveness of our proposed method.