NEclatClosed: A vertical algorithm for mining frequent closed itemsets

Aryabarzan, Nader; Minaei-Bidgoli, Behrouz

doi:10.1016/j.eswa.2021.114738

Cited by 15 publications

(8 citation statements)

References 41 publications

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“…The details are shown in Table 2. The proposed CFI_GA algorithm has been compared with the existing FPClose [53], DCI_Closed [54], N-list-based algorithm for mining frequent closed patterns (NAFCP) [55], and NEclatClosed [56] algorithms. The graphical representation of run time on the different datasets is illustrated in Figure 7.…”

Section: Resultsmentioning

confidence: 99%

A genetic algorithm coupled with tree-based pruning for mining closed association rules

Poovan

Acharya

Reddy

2023

IJECE

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Due to the voluminous amount of itemsets that are generated, the association rules extracted from these itemsets contain redundancy, and designing an effective approach to address this issue is of paramount importance. Although multiple algorithms were proposed in recent years for mining closed association rules most of them underperform in terms of run time or memory. Another issue that remains challenging is the nature of the dataset. While some of the existing algorithms perform well on dense datasets others perform well on sparse datasets. This paper aims to handle these drawbacks by using a genetic algorithm for mining closed association rules. Recent studies have shown that genetic algorithms perform better than conventional algorithms due to their bitwise operations of crossover and mutation. Bitwise operations are predominantly faster than conventional approaches and bits consume lesser memory thereby improving the overall performance of the algorithm. To address the redundancy in the mined association rules a tree-based pruning algorithm has been designed here. This works on the principle of minimal antecedent and maximal consequent. Experiments have shown that the proposed approach works well on both dense and sparse datasets while surpassing existing techniques with regard to run time and memory.

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Section: Resultsmentioning

confidence: 99%

A genetic algorithm coupled with tree-based pruning for mining closed association rules

Poovan

Acharya

Reddy

2023

IJECE

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“…Periodic sequential pattern mining [20,21] is used to fnd patterns that occur frequently and periodically in long sequences. Subgraph mining [22,23] is another feld of sequential pattern mining, which aims to discover all frequent subgraphs in graph databases, the corresponding algorithms based on diferent data structures (such as liststructure [24], pattern-tree [25], and optimization algorithm [19]) are proposed to solve the related pattern mining problem from sequences database, and all these pattern mining approaches are based on the sequential database and the constraints threshold (selected by the user). Recently, Wang gives Miner-K [25] algorithm to mine the patterns with length constraints, and Nader proposes NEclat-Closed [24] to mine the closed pattern based on a vertical structure.…”

Section: Related Workmentioning

confidence: 99%

“…For a given test level α, a testable support requirement by Barnard's test statistic [24] is given by the following:…”

Section: Permutation Testing and Test Statistic Selectionmentioning

confidence: 99%

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Discovering Significant Sequential Patterns in Data Stream by an Efficient Two-Phase Procedure

Tang

Wang

Liu

et al. 2022

Mathematical Problems in Engineering

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One essential topic of mining sequential patterns in the data stream is to optimize the time-space computations. However, more importantly, it should pay more attention to the significance of mining results as a large portion of them just response to the user-defined constraints purely by accident and they may have no statistical significance. In this paper, we propose FSSPDS, an efficient two-phase algorithm to discover the significant sequential patterns (SSPs) in the data stream with typical sliding windows, which has never been considered in existing problems. First, for generating SSPs candidates with high-quality, FSSPDS takes testable support and pattern length constraints into account and insignificant patterns were removed timely by a pattern-growth method. In the second phase, appropriate permutation testing is used to test the significance of the SSPs candidates. Exact permutation p values are obtained in a novel combination way based on unconditional Barnard’s test statistic which better reflects the process of data generations and collections. Experimental evaluations show that FSSPDS allows the discovery of SSPs in the data stream and rivals the state-of-the-art approaches efficiently under the control of family-wise error rate (FWER), especially for time efficiency, which was approximately an order of magnitude higher.

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“…Therefore, FP-Growth can conveniently obtain all interesting itemsets and it requires fewer resources than Apriori. In general, numerous investigators have improved FIM algorithms in recent decades [24], [27]- [29]. However, as explained in Section I, most FIM algorithms do not consider information regarding the unit interesting factor of items.…”

Section: A Frequent Itemset Miningmentioning

confidence: 99%

TargetUM: Targeted High-Utility Itemset Querying

Miao¹,

Wan²,

Gan³

et al. 2021

Preprint

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Traditional high-utility itemset mining (HUIM) aims to determine all high-utility itemsets (HUIs) that satisfy the minimum utility threshold (minUtil) in transaction databases. However, in most applications, not all HUIs are interesting because only specific parts are required. Thus, targeted mining based on user preferences is more important than traditional mining tasks. This paper is the first to propose a target-based HUIM problem and to provide a clear formulation of the targeted utility mining task in a quantitative transaction database. A tree-based algorithm known as Target-based high-Utility iteMset querying using (TargetUM) is proposed. The algorithm uses a lexicographic querying tree and three effective pruning strategies to improve the mining efficiency. We implemented experimental validation on several real and synthetic databases, and the results demonstrate that the performance of TargetUM is satisfactory, complete, and correct. Finally, owing to the lexicographic querying tree, the database no longer needs to be scanned repeatedly for multiple queries.

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NEclatClosed: A vertical algorithm for mining frequent closed itemsets

Cited by 15 publications

References 41 publications

A genetic algorithm coupled with tree-based pruning for mining closed association rules

A genetic algorithm coupled with tree-based pruning for mining closed association rules

Discovering Significant Sequential Patterns in Data Stream by an Efficient Two-Phase Procedure

TargetUM: Targeted High-Utility Itemset Querying

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