Improved Apriori Algorithm for Mining Association Rules
TL;DR: Proposed algorithm reduces one redundant pruning operations of Apriori algorithm for the two bottlenecks of frequent itemsets mining: the large multitude of candidate 2- itemsets, the poor efficiency of counting their support.
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Abstract: Association rules are the main technique for data mining. Apriori algorithm is a classical algorithm of association rule mining. Lots of algorithms for mining association rules and their mutations are proposed on basis of Apriori algorithm, but traditional algorithms are not efficient. For the two bottlenecks of frequent itemsets mining: the large multitude of candidate 2- itemsets, the poor efficiency of counting their support. Proposed algorithm reduces one redundant pruning operations of
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Improving Efficiency of Apriori Algorithm Using Transaction Reduction
Jaishree Singh,Hari Ram +1 more
- 01 Jan 2013
TL;DR: An Improved Apriori algorithm is proposed which reduces the scanning time by cutting down unnecessary transaction records as well as reduce the redundant generation of sub-items during pruning the candidate itemsets, which can form directly the set of frequent itemsets and eliminate candidate having a subset that is not frequent.
88
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References
Mining association rules between sets of items in large databases
Rakesh Agrawal,Tomasz Imielinski,Arun N. Swami +2 more
- 01 Jun 1993
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
Mining sequential patterns
Rakesh Agrawal,Ramakrishnan Srikant +1 more
- 06 Mar 1995
TL;DR: Three algorithms are presented to solve the problem of mining sequential patterns over databases of customer transactions, and empirically evaluating their performance using synthetic data shows that two of them have comparable performance.
Dynamic itemset counting and implication rules for market basket data
Sergey Brin,Rajeev Motwani,Jeffrey D. Ullman,Shalom Tsur +3 more
- 01 Jun 1997
TL;DR: A new algorithm for finding large itemsets which uses fewer passes over the data than classic algorithms, and yet uses fewer candidate itemsets than methods based on sampling and a new way of generating “implication rules” which are normalized based on both the antecedent and the consequent.
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•Proceedings Article
An Efficient Algorithm for Mining Association Rules in Large Databases
Ashoka Savasere,Edward Omiecinski,Shamkant B. Navathe +2 more
- 11 Sep 1995
TL;DR: This paper presents an efficient algorithm for mining association rules that is fundamentally different from known algorithms and not only reduces the I/O overhead significantly but also has lower CPU overhead for most cases.