BitTableFI: An efficient mining frequent itemsets algorithm

Dong, Jie; Han, Min Wha

doi:10.1016/j.knosys.2006.08.005

Cited by 110 publications

(55 citation statements)

References 16 publications

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“…According to the literature, besides Agarwal and Srikant themselves [1], many researchers endeavored in improving Apriori algorithm [9][10][11][12][13]. For instance, Park et al proposed Direct Hashing and Pruning algorithm that utilizes a hash approach to geneate candidate itemsets [9].…”

Section: Related Workmentioning

confidence: 99%

“…Sampling is a powerful data reduction technique that has been applied to a variety of problems in database systems [11]. Dong and Han proposed an algorithm named as BitTableFI, which compresses the database into BitTable, and candidate itemsets generation and support count can be performed quickly with the special data structure [12]. The I-Apriori algorithm, proposed by Bhandari et al in 2015, is a combination of FP-tree and Apriori algorithm.…”

Section: Related Workmentioning

confidence: 99%

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An improved Apriori algorithm for mining association rules

Yuan

2017

AIP Conference Proceedings

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An improved Apriori algorithm for mining association rules

Yuan

2017

AIP Conference Proceedings

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“…Dong [5] designed a BitTableFI algorithm to compress database for quick candidate itemsets generation. After that, the Index-BitTableFI algorithm is proposed by Song [6] , to avoid the redundant operations on frequent itemsets checking.…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate the efficiency of this proposed algorithm, experiments are carried out among Apriori, CBAR [4] , BitTableFI [6] and MIbARM with two synthetic datasets, D10K.T10.I5.N5 and D50K.T20.I10.N5, which are provided by IBM Almaden Quest research group [3,5] The running time of those four algorithms on two synthetic datasets is shown in Fig.1, with Fig.2 and Fig.3 From Fig.2 and Fig.3, the rule number of interestingness-based MIbARM is less than that of Apriori based on support and confidence, under 64 different conditions of parameter combinations. In Apriori, a mass of redundant rules are conducted with rapid decreasing of the Sup min , which makes the number of association rules increase exponentially.…”

Section: Performance Evaluationmentioning

confidence: 99%

An Efficient Association Rule Mining Me-thod for Personalized Recommendation in Mobile E-commerce

Deng¹,

Jin²,

Higuchi³

et al. 2010

Proceedings of the 2010 International Conference on E-Business Intelligence

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The association rule mining (ARM) is an important method to solve personalized recommendation problem in e-commerce. However, when applied in personalized recommendation system in mobile ecommerce(MEC), traditional ARMs are with low mining efficiency and accuracy. To enhance the efficiency in obtaining frequent itemsets and the accuracy of rules mining, this paper proposes an algorithm based on matrix and interestingness, named MIbARM, which only scans the database once, can deletes infrequent items in the mining process to compressing searching space. Finally, experiments among Apriori, CBAR and BitTableFI with two synthetic datasets and 64 different parameter combinations were carried out to verify MIbARM. The results show that the MIbARM succeed to avoid redundant candidate itemsets and significantly reduce the number of redundant rules, and it is efficient and effective for personalized recommendation in MEC.

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“…For this work, mining frequent itemset, researched first by Agrawal et al [1] in 1993, has become more and more important and many new algorithms or improvements have been proposed to solve the problem more efficiently, such as Eclat [36], FP-Growth [18], FPGrowth* [14], BitTable-FI [12] and Index-BitTableFI [29].…”

Section: Introductionmentioning

confidence: 99%

Structure of frequent itemsets with extended double constraints

Tin

Hải

Ngan

2016

Vietnam J Comput Sci

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Frequent itemset discovering has been one essential task in data mining. In the worst case, the cardinality of the class of all frequent itemsets is of exponent which leads to many difficulties for users. Therefore, a model of constraintbased mining is necessary when their needs and interests are the top priority. This paper aims to find a structure of frequent itemsets that satisfy the following conditions: they include a subset C 10 , contain no items of a subset C 11 , and have at least an item belonging to subset C 21 . The first new point of the paper is the proposed theoretical result that is the generalization of our former researches (Hai et al. in Adv Comput Methods Knowl Eng Sci 479:367-378, 2013). Second, based on new sufficient and necessary conditions discovered just for closed itemsets and their generators in association with the methods of creating borders and eliminating branches and nodes on the lattice, we can effectively and quickly eliminate not only a class of frequent itemsets but also one or more branches of equivalence classes of which elements are insatiate the constraints. Third, a structure and a unique representation of frequent itemsets with extended double constraints are shown by representative closed itemsets and their generators. Finally, all theoretical results in this paper are proven to be reliable and they are firm bases to guarantee the correctness and efficiency of a new algorithm, MFS-EDC, which is used to effectively mine all constrained frequent itemsets. Experiments show the outstanding efficiency of this

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BitTableFI: An efficient mining frequent itemsets algorithm

Cited by 110 publications

References 16 publications

An improved Apriori algorithm for mining association rules

An improved Apriori algorithm for mining association rules

An Efficient Association Rule Mining Me-thod for Personalized Recommendation in Mobile E-commerce

Structure of frequent itemsets with extended double constraints

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