Smart frequent itemsets mining algorithm based on FP-tree and DIFFset data structures

Gatuha, George; Jiang, Tao

doi:10.3906/elk-1602-113

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…The negFIN algorithm (Aryabarzan et al, 2018) adopts the bitwise operation to extract the NegNodesets structure of the itemset and uses a set enumeration tree to generate frequent itemsets, which is pruned by the promotion method. FDM is a new algorithm based on FP-tree and DIFFset (Gatuha & Jiang, 2017) data structures, which can mine long and short patterns from dense and sparse datasets. The SS-FIM algorithm (Djenouri et al, 2017) performs a single scan of the transactional database to extract frequent itemsets, and the algorithm is less sensitive to the changes in the minimum support threshold set by the user.…”

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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“…In the FP tree establishment step, [35] the database is scanned to identify the highfrequency itemsets that meet the minimum degree of support, and they are arranged according to the support degree, as shown in Table 3. After the database is scanned, the support degree of each itemset is compared with the minimum support degree, and the itemset with an insufficient threshold value is be deleted, as shown in Table 4.…”

Section: The Formula Is As Follows: Confidence (A⇒b) = P(a∩b)/p(a)mentioning

confidence: 99%

Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis

et al. 2021

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Storage devices in the computer industry have gradually transformed from the hard disk drive (HDD) to the solid-state drive (SSD), of which the key component is error correction in not-and (NAND) flash memory. While NAND flash memory is under development, it is still limited by the “program and erase” cycle (PE cycle). Therefore, the improvement of quality and the formulation of customer service strategy are topics worthy of discussion at this stage. This study is based on computer company A as the research object and collects more than 8000 items of SSD error data of its customers, which are then calculated with data mining and frequent pattern growth (FP-Growth) of the association rule algorithm to identify the association rule of errors by setting the minimum support degree of 90 and the minimum trust degree of 10 as the threshold. According to the rules, three improvement strategies of production control are suggested: (1) use of the association rule to speed up the judgment of the SSD error condition by customer service personnel, (2) a quality strategy, and a (3) customer service strategy.

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“…Many previous works [10], [26], [27], [28], [29], [30], [31], [33], [34], [35] have shown that pattern growth approaches based on prefix trees are more efficient than candidate generation-and-test approaches. However, pattern growth approaches are confronted with a problem that they have to spend much time in constructing many (conditional) prefix trees.…”

Section: A Motivationmentioning

confidence: 99%

Efficient Mining of Frequent Itemsets Using Only One Dynamic Prefix Tree

Hang

Zhao

et al. 2020

IEEE Access

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Frequent itemset mining is a fundamental problem in data mining area because frequent itemsets have been extensively used in reasoning, classifying, clustering, and so on. To mine frequent itemsets, previous algorithms based on a prefix tree structure have to construct many prefix trees, which is very time-consuming. In this paper, we propose a novel frequent itemset mining algorithm called DPT (Dynamic Prefix Tree) which uses only one prefix tree. We first introduce the concept of the postconditional database of an itemset, and analyze the distribution of an itemset's post-conditional database in a prefix tree representing a database. Subsequently, we illuminate how DPT adjusts the prefix tree to mine frequent itemsets and give three optimization techniques. An interesting advantage of DPT is that the algorithm can directly output a prefix tree representing all frequent itemsets after slight modifications. Using only one dynamic prefix tree, DPT avoids the high cost of constructing many prefix trees and thus gains significant performance improvement. Experimental results show that DPT remarkably outperforms previous algorithms with respect to running time and memory usage, and that a prefix tree representing all frequent itemsets DPT outputs can be used more efficient than a list representing them previous algorithms output.

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Smart frequent itemsets mining algorithm based on FP-tree and DIFFset data structures

Cited by 8 publications

References 12 publications

Frequent Itemset Mining Algorithm Based on Linear Table

Frequent Itemset Mining Algorithm Based on Linear Table

Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis

Efficient Mining of Frequent Itemsets Using Only One Dynamic Prefix Tree

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