A novel pruning algorithm for mining long and maximum length frequent itemsets

Lessanibahri, Sina; Gastaldi, Luca; Fernández, Camino

doi:10.1016/j.eswa.2019.113004

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…In addition, the AFARTICA algorithm (Paladhi et al, 2019) proposed a pruning technique based on artificial cell division (ACD) to solve the problem of multiple search spaces. Lessanibahri et al (2020) proposed a pruning method named LengthSort, which prunes items and transactions before building a frequent pattern tree structure, thereby reducing the search space.…”

Section: Related Workmentioning

confidence: 99%

Frequent Itemset Mining Algorithm Based on Linear Table

Zhou

et al. 2023

Journal of Database Management

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Aiming at the speed of frequent itemset mining, a new frequent itemset mining algorithm based on a linear table is proposed. The linear table can store more shared information and reduce the number of scans to the original dataset. Furthermore, operations such as pruning and grouping are also used to optimize the algorithm. For different datasets, the algorithm shows different mining speeds. (1) In sparse datasets, the algorithm achieves an average 45% improvement in mining speed over the bit combination algorithm, and a 2-3 times improvement for the classic FP-growth algorithm. (2) In dense datasets, the average improvement over the classic FP-growth algorithm is 50-70%. For the bit combination algorithm, there are dozens of times of improvement. In fact, the algorithm that integrates bit combinations with bitwise AND operation can effectively avoid recursive operations and it is beneficial to the parallelization. Further analysis shows that the linear table is easy to split to facilitate the data batch mining processing.

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Section: Related Workmentioning

confidence: 99%

Frequent Itemset Mining Algorithm Based on Linear Table

Zhou

et al. 2023

Journal of Database Management

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“…Several pruning methods were developed to achieve this. A novel pruning algorithm LengthSort was conferred by Lessanibahri et al [15] to extract frequent Items that satisfy a given length threshold. An alternative approach that comprises removing irrelevant features and rows from datasets of higher dimensions was put forward by Vanahalli and Patil [16].…”

Section: Introductionmentioning

confidence: 99%

A multithreaded hybrid framework for mining frequent itemsets

Poovan

Acharya

Reddy³

2022

IJECE

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<p><span>Mining frequent itemsets is an area of data mining that has beguiled several researchers in recent years. Varied data structures such as Nodesets, DiffNodesets, NegNodesets, N-lists, and Diffsets are among a few that were employed to extract frequent items. However, most of these approaches fell short either in respect of run time or memory. Hybrid frameworks were formulated to repress these issues that encompass the deployment of two or more data structures to facilitate effective mining of frequent itemsets. Such an approach aims to exploit the advantages of either of the data structures while mitigating the problems of relying on either of them alone. However, limited efforts have been made to reinforce the efficiency of such frameworks. To address these issues this paper proposes a novel multithreaded hybrid framework comprising of NegNodesets and N-list structure that uses the multicore feature of today’s processors. While NegNodesets offer a concise representation of itemsets, N-lists rely on List intersection thereby speeding up the mining process. To optimize the extraction of frequent items a hash-based algorithm has been designed here to extract the resultant set of frequent items which further enhances the novelty of the framework.</span></p>

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