Dynamic Mining Hierarchical Topic from Web News Stream Data Using Divisive-Agglomerative Clustering Method

摘要

Given the popularity of Web news services, we focus our attention on mining hierarchical topic-from Web news stream data. To address this problem, we present a Divisive-Agglomerative clustering method to find hierarchical topic from Web news stream. The novelty of the proposed algorithm is the ability to identify meaningful news topics while reducing the amount of computations by maintaining cluster structure incrementally. Our streaming news clustering algorithm also works by leveraging off the nearest neighbors of the incoming streaming news datasets and has ability of identifying the different shapes and different densities of clusters. Experimental results demonstrate that the proposed clustering algorithm produces high-quality topic discovery.