An Incremental Web Document Clustering Based on the Transitive Closure Tree
Youn Sung-Dae, Suc-Bum Ko · Journal of Korea Multimedia Society · 2006
In document clustering methods, the k-means algorithm and the Hierarchical Alglomerative Clustering(HAC) are often used. The k-means algorithm has the advantage of a processing time and HAC has also the advantage of a precision of classification. But both methods have mutual drawbacks, a slow processing time and a low quality of classification for the k-means algorithm and the HAC, respectively. Also both methods have the serious problem which is to compute a document similarity whenever new document is inserted into a cluster. A main property of web resource is to accumulate an information by adding new documents frequently. Therefore, we propose a new method of transitive closure tree based on the HAC method which can improve a processing time for a document clustering, and also propose a superior incremental clustering method for an insertion of a new document and a deletion of a document contained in a cluster. The proposed method is compared with those existing algorithms on the basis of a pre챠sion, a recall, a F-Measure, and a processing time and we present the experimental results.