Web usage mining is the major application of data mining approach to learn usage patterns from Web data, with the intention of enhanced understanding and serve the requirements of Web-based applications. It is the major application of data mining techniques to web log data repositories. Web usage mining includes three most important steps: Data Preprocessing, Knowledge Extraction and Analysis of Extracted Results. Knowledge Extraction step is used in finding the user access patterns from web access log. Clustering is one of the typical algorithms in the field of data mining. The main objective of this paper is to organize a website into a set of clusters, which consists of "similar" data items based on the user behavior and navigation patterns. There are numerous clustering algorithms like K-Means, Possibilistic C-Means, etc., developed by many researchers. In recent times, Fuzzy C-Means is found to be superior as its embedded fuzzy logic. In noisy atmosphere, the memberships of FCM constantly do not correspond well to the degree of belonging of the data, and might be inexact. This paper uses a novel clustering algorithm called fuzzy-possibilistic C-Means (FPCM) algorithm, which integrates extended partition entropy and inter class resemblance which is computed from the fuzzy set point of view. The proposed approach uses FPCM to find out the user behavior since it needs only the membership matrix and possibilistic matrix, and is free from heavy distance computing. © 2011 EuroJournals Publishing, Inc.