Research Article
An Improved UP-Growth High Utility Itemset Mining
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 58 - Issue 2 |
| Published: November 2012 |
| Authors: Adinarayanareddy B, O. Srinivasa Rao, Mhm Krishna Prasad |
10.5120/9255-3424
|
Adinarayanareddy B, O. Srinivasa Rao, Mhm Krishna Prasad . An Improved UP-Growth High Utility Itemset Mining. International Journal of Computer Applications. 58, 2 (November 2012), 25-28. DOI=10.5120/9255-3424
@article{ 10.5120/9255-3424,
author = { Adinarayanareddy B,O. Srinivasa Rao,Mhm Krishna Prasad },
title = { An Improved UP-Growth High Utility Itemset Mining },
journal = { International Journal of Computer Applications },
year = { 2012 },
volume = { 58 },
number = { 2 },
pages = { 25-28 },
doi = { 10.5120/9255-3424 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2012
%A Adinarayanareddy B
%A O. Srinivasa Rao
%A Mhm Krishna Prasad
%T An Improved UP-Growth High Utility Itemset Mining%T
%J International Journal of Computer Applications
%V 58
%N 2
%P 25-28
%R 10.5120/9255-3424
%I Foundation of Computer Science (FCS), NY, USA
Abstract
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.
References
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