Improving Frequent-Term Based Text Clustering with Word Belief Network

Abstract

The algorithm of frequent-term based text clustering (FTC) can be applied to news topic clustering system, in order to help users locate interested topics and articles quickly. But it is difficult to set support threshold for mining association rules. This paper tries to build a word belief network, which satisfies basic rules of small worlds. So we can improve FTC algorithm with characteristics of small worlds and implement text clustering quickly. The paper puts forward an idea that adopts inverted index into this algorithm, which simplifies programming and improves operation efficiency. The experimental results verified that the system could find current hot news topics efficiently and users could locate their interested document collection.