Mining of High Utility Itemsets in Service Oriented Computing

Service Oriented Computing which use Knowledge as a service makes the use of Utility Mining approach. Here, we have proposed an architecture called Knowledge as a Service (KaaS) where we use utility mining algorithms for extracting the knowledge data from the data owners when the knowledge consumers are in need of a particular knowledge data. The main motive behind proposing architecture is to provide Utility Mining as a service in a distributed computing environment which can be applied in business such as cross selling approach.Efficient discovery of frequent itemsets in large datasets is a crucial task of data mining. In recent years, several approaches have been proposed for generating high utility patterns, they arise the problems of producing a large number of candidate itemsets for high utility itemsets and probably degrades mining performance in terms of speed and space. Recently proposed compact tree structure, viz., UP-Tree, maintains the information of transactions and itemsets, facilitate the mining performance and avoid scanning original database repeatedly. In this paper, UPTree (Utility Pattern Tree) is adopted, which scans database only twice to obtain candidate items and manage them in an efficient data structured way. Applying UP-Tree to the UP-Growth takes more execution time for Phase II. Hence this paper presents modified algorithm aiming to reduce the execution time by effectively identifying high utility itemsets.