A Network Decomposition-based Text Clustering Algorithm for Topic Detection

Text clustering is one of the most popular topic detection techniques. However, the existing text clustering approaches require that each document has to be partitioned to one and only one cluster. This is not reasonable in some cases for there exist some documents which should not used to constitute topics. This paper firstly models a text document set as a network and designs a method for decomposing such a network, and then proposes a truly original text clustering algorithm for topic detection, called a network decomposition-based text clustering algorithm for topic detection (NDTCATD). The proposed algorithm ensures that meaningless documents can not be used to constitute topics. Experimental results show that NDTCATD is much better than bisecting k-means algorithm in terms of overall similarity and average cluster similarity. Therefore the proposed algorithm is reasonable and effective and is especially suitable for topic detection.