Web search applications represent user information needs by submission of query to search engine. But still the entire query submitted to search engine doesn"t satisfy the user information needs, because users may want to get information on diverse aspects when they submit the same query. From this discovering the numeral of dissimilar user search goals for query and depicting each goal with several keywords automatically become complicated. The suggestion and examination of user search goals

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... n be very valuable in improving search engine importance and user knowledge. The numeral of dissimilar user search goals for query by k-means clustering is discovered by user feedback sessions. Pseudo document with k-means clustering is generated by user feedback sessions. Clustering Pseudo documents with k-means clustering results are computationally difficult and semantic similarity between the pseudo terms is also important while clustering. To conquer this problem proposed a FCM (fuzzy c means) clustering algorithm to group the pseudo documents and it also measure the semantic similarity between the pseudo terms in the documents. The FCM algorithm divides pseudo documents data for dissimilar size cluster by using fuzzy systems. FCM choosing cluster size and central point depend on fuzzy model. The FCM clustering algorithm it congregate quickly to a local optimum or grouping of the pseudo documents in well-organized way. Semantic similarity between the pseudo terms with keywords based similarity is used for comparing the similarity and diversity of pseudo terms. Finally experimental result measures the clustering results with parameters like classified average precision (CAP), Voted AP (VAP), risk to avoid classifying search results and average precision (AP). It shows FCM based system improve the feedback sessions outcome than the normal pseudo documents.