Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining

Zhang, Yalong; Yu, Wei; Ma, Xuan; Ogura, Hisakazu; Ye, Dongfen

doi:10.3390/app11198971

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…One solution is to innovate new algorithms to improve operational efficiency. For example, Zhang et al [23] proposed a multi-objective optimization algorithm to mine frequent itemsets of high-dimensional data. The other is to improve the evaluation method of association rules.…”

Section: Evaluation Methods Of Association Rulesmentioning

confidence: 99%

An Improved Evaluation Methodology for Mining Association Rules

Bao

Mao

Zhu

et al. 2021

Axioms

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At present, association rules have been widely used in prediction, personalized recommendation, risk analysis and other fields. However, it has been pointed out that the traditional framework to evaluate association rules, based on Support and Confidence as measures of importance and accuracy, has several drawbacks. Some papers presented several new evaluation methods; the most typical methods are Lift, Improvement, Validity, Conviction, Chi-square analysis, etc. Here, this paper first analyzes the advantages and disadvantages of common measurement indicators of association rules and then puts forward four new measure indicators (i.e., Bi-support, Bi-lift, Bi-improvement, and Bi-confidence) based on the analysis. At last, this paper proposes a novel Bi-directional interestingness measure framework to improve the traditional one. In conclusion, the bi-directional interestingness measure framework (Bi-support and Bi-confidence framework) is superior to the traditional ones in the aspects of the objective criterion, comprehensive definition, and practical application.

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Section: Evaluation Methods Of Association Rulesmentioning

confidence: 99%

An Improved Evaluation Methodology for Mining Association Rules

Bao

Mao

Zhu

et al. 2021

Axioms

View full text Add to dashboard Cite

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“…The process involved in mining the frequent item aims to detect the item set where the occurrence frequency exceeds the present frequency of massive databases like big data. The process involved in mining the redundant item offers different types of tasks regarding correlation analysis, local periodicity and plotting the fragments [4]. The detection of frequent item sets involves various types of resources which help in mining the frequent items and diminishes the burden of the process [5].…”

Section: Introductionmentioning

confidence: 99%

A Progressive Sampling and RadeMacher Average for an Effective Frequent Pattern Mining in Big Data Environment

2023

IJIES

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Big data refers to the large amount of information that is collected from different areas and shared on the internet. However, this development has led to difficulties in using frequent itemset mining applications. To overcome the issue of frequent data mining, this research has introduced an empirical sampling algorithm using RadeMacher average (ESA-RMA). When considering the size of the initial sample and scheduling the samples, the ESA utilizes the RadeMacher average to bound the samples. Initially, the data is obtained from the dataset of the human activity recognition (HAR) and real time datasets from smartphone gyroscope and accelerometer, then obtained data is pre-processed using the data normalization technique. Then, the ESA is used to select the labelled data and RMA is used to bound the samples. This bounding process defines the upper limit of the input data which helps in the effective mining of frequent item sets. Thus, the data with redundant items are mined out using the proposed ESA-RMA method. The experimental results show that the proposed ESA-RMA has taken a minimum run time of 212 ms for data obtained from smartphone accelerometer which is comparatively lower than the existing Scalable Simple Random Sampling (ScaSRS) with processing time of 362 ms. Similarly, for HAR dataset, the proposed method took processing time of 5.43 s whereas the existing vertical frequent time interval-related pattern (VertTIRP) mining approach took processing time of 7.82 s.

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An algebraic semigroup method for discovering maximal frequent itemsets

Liu

Feng

et al. 2022

Open Mathematics

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Discovering maximal frequent itemsets is an important issue and key technique in many data mining problems such as association rule mining. In the literature, generating maximal frequent itemsets proves either to be NP-hard or to have O ( l 3 4 l ( m + n ) ) O\left({l}^{3}{4}^{l}\left(m+n)) complexity in the worst case from the perspective of generating maximal complete bipartite graphs of a bipartite graph, where m m , n n are the item number and the transaction number, respectively, and l l denotes the maximum of ∣ C ∣ ∣ Ψ ( C ) ∣ / ( ∣ C ∣ + ∣ Ψ ( C ) ∣ − 1 ) | C| | \Psi \left(C)| \hspace{0.1em}\text{/}\hspace{0.1em}\left(| C| +| \Psi \left(C)| -1) , with the maximum taken over all maximal frequent itemsets C C . In this article, we put forward a method for discovering maximal frequent itemsets, whose complexity is O ( 3 m n 2 β + 4 β n ) O\left(3mn{2}^{\beta }+{4}^{\beta }n) , lower than the known complexity both in the worst case, from the perspective of semigroup algebra, where β \beta is the number of items whose support is more than the minimum support threshold. Experiments also show that an algorithm based on the algebraic method performs better than the other three well-known algorithms. Meanwhile, we explore some algebraic properties with respect to items and transactions, prove that the maximal frequent itemsets are exactly the simplified generators of frequent itemsets, give a necessary and sufficient condition for a maximal i + 1 i+1 -frequent itemset being a subset of a closed i i -frequent itemset, and provide a recurrence formula of maximal frequent itemsets.

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Multi-Objective Optimization for High-Dimensional Maximal Frequent Itemset Mining

Cited by 5 publications

References 26 publications

An Improved Evaluation Methodology for Mining Association Rules

An Improved Evaluation Methodology for Mining Association Rules

A Progressive Sampling and RadeMacher Average for an Effective Frequent Pattern Mining in Big Data Environment

An algebraic semigroup method for discovering maximal frequent itemsets

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