An optimized FP-growth algorithm for discovery of association rules

Shawkat, Mai; Badawi, Mahmoud; Elghamrawy, Sally M.; Arnous, Reham; El-Desoky, Ali Ibrahim

doi:10.1007/s11227-021-04066-y

Cited by 30 publications

(16 citation statements)

References 34 publications

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“…The FP growth algorithm is an association mining algorithm proposed by Han et al for the shortcomings of the Apriori algorithm, This algorithm first compresses the database into a frequent pattern tree and then divides the compressed database into a set of conditional databases, each associated with a frequent item, and mines each conditional database separately. Finally, all mining results are aggregated to obtain a set of frequent items [ 21 ]. Advantages: ① FP growth algorithm only needs to traverse the data set twice, so it is faster [ 22 ].…”

Section: Related Workmentioning

confidence: 99%

“…If different paths have the same prefix path and share storage space, the data is compressed. Disadvantages: ① The second traversal of FP tree will store many intermediate values, which will occupy a lot of memory [ 24 ]. ② Constructing an FP tree is expensive [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

“…① The second traversal of FP tree will store many intermediate values, which will occupy a lot of memory [ 24 ]. ② Constructing an FP tree is expensive [ 25 ].…”

Section: Related Workmentioning

confidence: 99%

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An Improved Apriori Association Rule for the Identification of Acupoints Combination in Treating COVID-19 Patients

Zheng

2022

Computational Intelligence and Neuroscience

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This work presents a data-driven method for identifying the potential core acupoint combination in COVID-19 treatment through mining the association rules from the retrieved scientific literature and guidelines for prevention and treatment of COVID-19 published all over China. It is based on the representation of the acupoint data in a binary form, the use of a novel association rule mining algorithm properly tailored for discovering the relationship of acupoint groups among combinations of different descriptions. The proposed method is applied to the real database of acupoint descriptions collected from published literature and guidelines. The obtained results show the effectiveness of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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An Improved Apriori Association Rule for the Identification of Acupoints Combination in Treating COVID-19 Patients

Zheng

2022

Computational Intelligence and Neuroscience

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“…They also proposed another FP-array approach [13] to minimize tree traversing time, but the recurring construction of conditional FP-trees still exists. Shawkat et al [14] presented a new scheme to discover associations from a wide range of relations across the dataset. Trough combining the FP-tree* mining method and header table of FP-growth they developed an algorithm as MFP-growth.…”

Section: Related Workmentioning

confidence: 99%

An Algorithm to Create Sorted FP-Growth Tree for Extracting Association Rules

Barkhan¹,

Ramazani

Kabani

2022

Preprint

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Mining data in massive databases to find useful data, frequent patterns (FP) and knowledge discovery, has been studied popularly in data mining research fields minding governments, industries and sales companies. In data mining, frequent patterns as growth tree are an efficient method for discovering knowledge and compressing information in a tree structure. Previous studies presented various methods to achieve frequent patterns which even require complex process and costs, particularly if the patterns overnumbered. In this paper we provide a divide and conquer algorithm based on FP-Growth Tree to create an initially sorted tree structure of the nodes that the most frequent patterns would be available at every moment of the tree construction procedure. In addition, we can consider a parameter to avoid inserting less frequent branches, thus, the tree would be sorted from the beginning of its formation.

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“…The best association rules were extracted using the Élimination Et Choix Traduisant La Realité (Elimination and Choice Translating Reality/ELECTRE) method [33]. A modified frequent pattern (FP)-growth algorithm is put forth by Shawkat et al [34] to overcome the performance gap while handling large datasets. The relationship between skill and job was analyzed using this algorithm [35].…”

Section: Introductionmentioning

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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An optimized FP-growth algorithm for discovery of association rules

Cited by 30 publications

References 34 publications

An Improved Apriori Association Rule for the Identification of Acupoints Combination in Treating COVID-19 Patients

An Improved Apriori Association Rule for the Identification of Acupoints Combination in Treating COVID-19 Patients

An Algorithm to Create Sorted FP-Growth Tree for Extracting Association Rules

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

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