An Efficient Algorithm for Identification of Most Valuable Itemsets from WebTransaction Log Data

Web Utility mining has recently been a bloomingtopic in the field of data mining and so is the web mining, animportant research topic in database technologies. Thus, theweb utility mining is effective in not only discovering thefrequent temporal web transactions & generating high utilityitemsets, but also identifying the profit of webpages. Forenhancing the web utility mining, this study proposes a mixedapproach to the techniques of web mining, temporal highutility itemsets& Onshelf utility mining algorithms, toprovide web designers and decision makers more useful and meaningful web information. In the two Phases of thealgorithm, we came out with the more efficient and moderntechniques of web & utility mining in order to yield excellentresults on web transactional databases. Mining most valuableitemsets from a transactional dataset refers to theidentification of the itemsets with high utility value as profits.Although there are various algorithms for identifying highutility itemsets, this improved algorithm is focused on onlineshopping transaction data. The other similar algorithmsproposed so far arise a problem that is they all generate largeset of candidate itemsets for Most Valuable Itemsets and alsorequire large number database scans. Generation of largenumber of item sets decreases the performance of mining withrespect to execution time and space requirement. This situationmay worse when database contains a large number oftransactions. In the proposed system, information of valuableitemsets are recorded in tree based data structure called UtilityPattern Tree which is a compact representation of items intransaction database. By the creation of Utility Pattern Tree,candidate itemsets are generated with only two scans of thedatabase. Recommended algorithms not only reduce a numberof candidate itemsets but also work efficiently when databasehas lots of long transactions.