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Content Provider | IEEE Xplore Digital Library |
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Author | Sheng Chai Jia Yang Yang Cheng |
Copyright Year | 2007 |
Description | Author affiliation: Sichuan Univ., Chengdu (Sheng Chai) |
Abstract | The efficiency of mining association rules is an important field of Knowledge Discovery in Databases. The Apriori algorithm is a classical algorithm in mining association rules. This paper presents an improved Apriori algorithm to increase the efficiency of generating association rules. This algorithm adopts a new method to reduce the redundant generation of sub-itemsets during pruning the candidate itemsets, which can form directly the set of frequent itemsets and eliminate candidates having a subset that is not frequent in the meantime. This algorithm can raise the probability of obtaining information in scanning database and reduce the potential scale of itemsets. |
Starting Page | 1 |
Ending Page | 4 |
File Size | 209503 |
Page Count | 4 |
File Format | |
ISBN | 1424408849 |
DOI | 10.1109/ICSSSM.2007.4280173 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2007-06-09 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Algorithm design and analysis association rule Itemsets frequent itemset data mining Apriori algorithm Frequency Educational institutions Transaction databases Data mining Association rules Testing |
Content Type | Text |
Resource Type | Article |
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