An Efficient System on High Utility Infrequent Itemsets Mining over Weblog Data

In recent years, mining infrequent itemsets over weblog databases have attracted much attention and their significance in many applications like fraud detection, web portals, information access and retrieval tools, giving information on problems occurred to the users, etc. The weblog is unformed data and contains information about User Name, IP Address, Time Stamp, Access-Request, number of Bytes Transferred, etc. The log files are maintained by the web servers. It gives details about the user. Infrequent Itemset mining differs from frequent itemset mining where it locates the uninteresting patterns, i.e., it detects the data items that arise very rarely. Itemsets which do not occur frequently in the database. All the itemsets which has value lesser than the support, will be considered as infrequent item sets. Data mining techniques like association rules, sequential patterns, clustering and classification can be used to discover frequent patterns. This paper aims to develop a novel dynamic algorithm for infrequent itemset over weblog data.