<![CDATA[The fast expansion of text information volume has caused the difficulty of infor-mation retrieval process, mainly on the model of word-based matching. The synonymy factor of word has caused non relevant document to be retrieved, whereas the polisemy factor has caused relevant document not to be retrieved. The application of document clustering to the search results before presented to the user can increase the effect-tiveness of retrieval. This study elaborates the application of document clustering to im-prove the effectiveness of retrieval by clustering to the search result before presented to the user. Three clustering algorithms from partitional approach i.e. K-Means, Bisecting K-Mean and Buckshot, and hierarchical agglomerative approach with two cluster similarity function i.e. UPGMA and Complete Link were chosen. The performance parameter was measured using F-measure, a metric derived from Precision and Recall of retrieval. The document collections to be tested are 1000 news document and 350 academic abstract documents. The results show that the presentation of search results by using clustering has improved the number of relevant document in the up-level ranks. The improvement was statistically significant compare to the page-rank method. The improvement of F-measure as a performance metric is about 14,34% for news documents and 28,18% for abstract documents.]]>
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