Abstract
This paper discusses about the significance of data preprocessing methods and various steps involved in getting the required content successfully. An entire preprocessing technique is being planned to preprocess the web log for extraction of user patterns. Data cleansing algorithm is applied to eliminate the extraneous entries from web log at the same time filtering algorithm is used to discard the impassive attributes from log file. The outlier are detected and removed from the dataset. The User and sessions are identified. The performance of the data cleansing process was evaluated by adapting the wrapper approach in which the resultant cleaned dataset are clustered using five different clustering algorithms namely Farthest First, K-means, COBWEB, make density based algorithm and Expectation maximization algorithm to identify the quality of web log data
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