DocumentCode :
2202519
Title :
A Frequent Item Graph Approach for Discovering Frequent Itemsets
Author :
Kumar, A. V Senthil ; Wahidabanu, R.S.D.
Author_Institution :
Dept. of MCA, CMS Coll. of Sci. & Commerce, Coimbatore
fYear :
2008
fDate :
20-22 Dec. 2008
Firstpage :
952
Lastpage :
956
Abstract :
Efficient algorithms to discover frequent patterns are crucial in data mining research. Finding frequent item sets is computationally the most expensive step in association rule discovery and therefore it has attracted significant research attention. In this paper, we present a more efficient approach for mining complete sets of frequent item sets. It is a modification of FP-tree. The contribution of this approach is to count the frequent 2-item sets and to form a graphical structure which extracts all possible frequent item sets in the database. We present performance comparisons for our algorithm against FP-growth algorithm.
Keywords :
data mining; database management systems; pattern recognition; FP-growth algorithm; FP-tree; association rule discovery; data mining; database; frequent item graph approach; frequent itemsets; frequent patterns; Association rules; Business; Collision mitigation; Data mining; Databases; Educational institutions; Heuristic algorithms; Itemsets; Marketing and sales; Partitioning algorithms; Association rules; data mining; frequent itemsets; minimum support;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Advanced Computer Theory and Engineering, 2008. ICACTE '08. International Conference on
Conference_Location :
Phuket
Print_ISBN :
978-0-7695-3489-3
Type :
conf
DOI :
10.1109/ICACTE.2008.129
Filename :
4737098
Link To Document :
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