文章基本信息

标题：An Efficient Algorithm for Identification of Most Valuable Itemsets from WebTransaction Log Data
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作者：Litty Tressa George ; Asha Rose Thomas
期刊名称：International Journal of Computer Techniques
电子版ISSN：2394-2231
出版年度：2015
卷号：2
期号：3
页码：116-122
语种：English
出版社：International Research Group - IRG
摘要：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, the web utility mining is effective in not only discovering the frequent temporal web transactions & generating high utility item sets, but also identifying the profit of webpages. For enhancing the web utility mining, this study proposes a mixed approach to the techniques of web mining, temporal high utility item sets& On shelf utility mining algorithms, to provide web designers and decision makers more useful and meaningful web information. In the two Phases of the algorithm, we came out with the more efficient and modern techniques 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 item sets, this improved algorithm is focused on online shopping transaction data. The other similar algorithm sproposed so far arise a problem that is they all generate large set of candidate item sets for Most Valuable Item sets and also require large number database scans. Generation of large number of item sets decreases the performance of mining with respect to execution time and space requirement.This situation may worse when database contains a large number of transactions. In the proposed system,information of valuable item sets are recorded in tree based data structure called Utility Pattern Tree which is a compact representation of items in transaction database. By the creation of Utility Pattern Tree,candidate item sets are generated with only two scans of the database. Recommended algorithms not only reduce a number of candidate item sets but also work efficiently when data base has lots of long transactions. Keywords —Utility Mining, Itemset utility, Valuable itemsets, Most valuable itemsets.