An improved apriori algorithm based on support weight matrix for data mining in transaction database

Sun, Lina

doi:10.1007/s12652-019-01222-4

Cited by 23 publications

(10 citation statements)

References 11 publications

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“…Sun et al (2020) built 0-1 transaction matrix by scanning transaction database to gain weighted support and confidence. The method can shorten the running time, reduce the memory demand and the number of operations, and effectively extract the hidden and valuable items [12].…”

Section: Introductionmentioning

confidence: 99%

Sustainable Development of College and University Education by use of Data Mining Methods

Wang

Chung

2021

Int. J. Emerg. Technol. Learn.

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To improve the education efficiency of the students, the student-centered education plan is explored. First, the Apriori algorithm of association rules is used to mine the potential related patterns in the score data of college students and establish a reasonable teaching method. Second, aided by the decision tree model, the factors affecting students' academic performance are studied, and the potential relationship between different courses is studied. Finally, the Apriori algorithm of association rules combined with decision tree model is used to generate the early warning mechanism of students' achievement, and the course performance of college students is empirically analyzed. The results show that: C language has two sides of dependence on many subjects; higher mathematics → linear algebra → mathematical statistics → computer composition principle → computer network. The teaching scheme of C language → C + + → Java more conforms to the learning mechanism of college students. Through empirical analysis, the early warning mechanism of association rule Apriori algorithm and decision tree model can effectively analyze student's course and give student's achievement. It is found that the method proposed can provide theoretical basis for students, teachers, and university administrators to carry out education reform and education management decision-making, improve students' performance and education quality, and realize the "student-oriented" education concept, so it can be applied to the actual education management.

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Section: Introductionmentioning

confidence: 99%

Sustainable Development of College and University Education by use of Data Mining Methods

Wang

Chung

2021

Int. J. Emerg. Technol. Learn.

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“…The current algorithms specific to frequent itemset mining are largely divided into two major types: exact algorithms and heuristic algorithms. The most classical exact algorithms are the Apriori algorithm [10] and FP-Growth algorithm [11], as well as many improved algorithms derived from the two algorithms [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining

Zhang

Zhu

et al. 2021

Applied Sciences

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When it comes to association rule mining, all frequent itemsets are first found, and then the confidence level of association rules is calculated through the support degree of frequent itemsets. As all non-empty subsets in frequent itemsets are still frequent itemsets, all frequent itemsets can be acquired only by finding all maximal frequent itemsets (MFIs), whose supersets are not frequent itemsets. In this study, an algorithm, named right-hand side expanding (RHSE), which can accurately find all MFIs, was proposed. First, an Expanding Operation was designed, which, starting from any given frequent itemset, could add items using certain rules and form some supersets of given frequent itemsets. In addition, these supersets were all MFIs. Next, this operator was used to add items by taking all frequent 1-itemsets as the starting point alternately, and all MFIs were found in the end. Due to the special design of the Expanding Operation, each MFI could be found. Moreover, the path found was unique, which avoided the algorithm redundancy in temporal and spatial complexity. This algorithm, which has a high operating rate, is applicable to the big data of high-dimensional mass transactions as it is capable of avoiding the computing redundancy and finding all MFIs. In the end, a detailed experimental report on 10 open standard transaction sets was given in this study, including the big data calculation results of million-class transactions.

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“…L.n. Sun [13] proposed an improved Apriori algorithm that builds a 01 transaction matrix by scanning a transaction database for obtaining its weighted support confidence. The items and transactions are weighted to reflect the importance of the transaction database.…”

Section: Related Workmentioning

confidence: 99%

Finding Frequent Items: A Novel Method for Improving the Apriori Algorithm

Karimtabar

Fard

2022

csci

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In the current paper, we use an intelligent method for improved the Apriori algorithm in order to extract frequent itemsets. PAA (proposed Apriori algorithm) is twofold. First, it is not necessary to take only one data item at each step. In fact, all possible combinations of the items could be generated at each step. Secondly, we can scan only some transactions instead of scanning all the transactions to obtain frequent itemset. For performance evaluation, we conducted three experiments with the traditional Apriori, BitTableFI, TDM-MFI, and MDC_Apriori algorithms. The results exhibit that due to the significant reduction in the number of transaction scans to obtain the itemset, the algorithm execution time is significantly reduced; as in the first experiment, the time spent to generate frequent items underwent a reduction by 52% compared to the algorithm in the first experiment. In the second experiment, the amount of time spent is equal to 65%, while in the third experiment, it is equal to 46%.

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An improved apriori algorithm based on support weight matrix for data mining in transaction database

Cited by 23 publications

References 11 publications

Sustainable Development of College and University Education by use of Data Mining Methods

Sustainable Development of College and University Education by use of Data Mining Methods

Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining

Finding Frequent Items: A Novel Method for Improving the Apriori Algorithm

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