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Appling FP-Growth Algorithm to Reduce the Data Log Storage Size in Web Mining

R. Kousalya, S. Leo Philomin Raj


Frequent Patterns are very important in knowledge discovery and web data mining process such as mining of association rules, correlations etc. Many existing incremental mining algorithms are Apriori-based, which are not easily adoptable to solve association rule mining and find out the frequent pattern easily. An earlier approach for frequent pattern mining using web logs for web usage mining is used and this approach is called as HFPA. In this approach HFPA, is the technique which is applied to mine association rules from web logs using normal Apriori algorithm, but with few adaptations for improving the interestingness of the rules produced and for applicability for web usage mining. So, we perform clustering of the user sessions extracted from the Web logs to partition the users into several homogeneous groups with similar activities and then extract user profiles from each cluster as a set of relevant URLs. Data mining techniques have been applied to extract usage patterns from Web log data, this process is known as Web usage mining. The implementation also concentrates on the storage reduction. The proposed system implemented FP-Growth algorithm to reduce the data log storage size.


Frequent Patterns, Apriori Algorithm, Association Rules, FP-Growth Algorithm

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