  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, UP Tree (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.
