Abstract: The web log data embed much of the user’s browsing behavior and the operational data generated through Internet end user interaction may contain noise. Which affect the knowledge based decision. Handling these noisy data is a major challenge. Null value handling is an important noise handling technique in relational data base system. In this work the issues related to null value are discussed and null value handling concept based on train data set is applied to real MANIT web server log. A prototype system based on Fuzzy C-means clustering techniques with trained data set is also proposed in this work. The proposed method integrates advantages of fuzzy system and also introduces a new criterion, which enhances the estimated accuracy of the approximation. The comparisons between different methods for handling null values are depicted. The result shows the effectiveness of the methods empirically on realistic web logs and explores the accuracy, coverage and performance of the proposed Models.
Keywords: Null value, web mining, k-means clustering, fuzzy C-means clustering, log records, log parser.