DOCUMENT CLUSTERING BY DYNAMIC HIERARCHICAL ALGORITHM BASED ON FUZZY SET TYPE-II FROM FREQUENT ITEMSET

Abstract

One of ways to facilitate process of information retrieval is by performing clustering toward collection of the existing documents. The existing text documents are often unstructured. The forms are varied and their groupings are ambiguous. This cases cause difficulty on information retrieval process. Moreover, every second new documents emerge and need to be clustered. Generally, static document clustering method performs clustering of document after whole documents are collected. However, performing re-clustering toward whole documents when new document arrives causes inefficient clustering process. In this paper, we proposed a new method for document clustering with dynamic hierarchy algorithm based on fuzzy set type - II from frequent itemset. To achieve the goals, there are three main phases, namely: determination of key-term, the extraction of candidates clusters and cluster hierarchical construction. Based on the experiment, it resulted the value of F-measure 0.40 for Newsgroup, 0.62 for Classic and 0.38 for Reuters. Meanwhile, time of computation when addition of new document is lower than to the previous static method. The result shows that this method is suitable to produce solution of clustering with hierarchy in dynamical environment effectively and efficiently. This method also gives accurate clustering result.