NDLI: PCAR: An Efficient Approach for Mining Association Rules

Content Provider	IEEE Xplore Digital Library
Author	Peien Feng Hui Zhang Qingying Qiu Zhaoxia Wang
Copyright Year	2008
Description	Author affiliation: State Key Lab. of CAD&CG, Zhejiang Univ., Hangzhou (Peien Feng; Hui Zhang; Qingying Qiu; Zhaoxia Wang)
Abstract	A lot of existing algorithms used for mining association rules identify frequent itemsets by the method of bottom-up combination of smaller frequent itemsets or top-down decomposing of larger infrequent itemsets, these methods result the large volumes of candidate itemsets. Actually as the supersets of infrequent items are infrequent itemsets, this paper presents a new efficient method, namely pruning-classification association rule (PCAR). PCAR combines minimum frequency items with minimum frequency itemsets. It firstly deletes infrequent items from itemsets, then classifies itemsets based on frequency of itemsets, finally discovers frequent itemsets. The number of candidate itemsets is greatly reduced and itemsets need not to be combined or decomposed, therefore, operation time and memory requirement could be decreased accordingly. This method has significant advantage in mining association rule at large volumes of items and small frequency of itemsets. It is proved by experiments that PCAR outperforms the well-known Apriori and CBAR algorithms.
Starting Page	605
Ending Page	609
File Size	323418
Page Count	5
File Format	PDF
ISBN	9780769533056
DOI	10.1109/FSKD.2008.408
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	PCAR Association rule Data structures Transaction databases Partitioning algorithms Apriori Data mining Association rules CBAR Itemsets Warehousing Clustering algorithms Frequency Fuzzy systems
Content Type	Text
Resource Type	Article

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