Customized frequent patterns mining algorithms for enhanced Top-Rank-K frequent pattern mining

Abdelaal, Areej; Abed, Sa’ed; Alshayeji, Mohammad H.; Allaho, Mohammad

doi:10.1016/j.eswa.2020.114530

Cited by 9 publications

(8 citation statements)

References 23 publications

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“…For the URP under the month-based long cycle, the spectral clustering algorithm is adopted to model the PBS rental data. The optimal clusters of URP under the month-based long cycle are five categories {M 1 1 , M 1 2 , M 1 3 , M 1 4 , M 1 5 }, which is shown as (a) to (e) in Figure 4. The average curve of the five categories is displayed in Figure 5.…”

Section: Analysis Results Of Urpmentioning

confidence: 99%

“…In order to obtain data in different formats, the cleaned data set {T ij , i = 1, 2, ..., n; j = 1, 2, ..., m} is first analyzed and processed online to form a threedimensional data cube [7,25]. Then the data cube set is sliced and drilled to obtain the three-time granularity data set {S mining are used to find the user's riding preferences on three-time granularity data set [1,18,33]. Finally, the user's riding preferences should be mapped.…”

Section: Proposed Urp Analysis Frameworkmentioning

confidence: 99%

“…Input: sample matrix {S1 il , i = 1, 2, ..., n; l = 1, 2, ..., p 1 } and the number of clusters c Output:clusters M 1 1 , M 1 2 , ..., M 1 xStep 1. construct the sample similarity matrix QStep 2. construct adjacency matrix W and degree matrix D based on sample similarity matrix QStep 3. calculate the Laplacian matrix L = D − W and the normalized Laplacian matrix D − 1 2 LD − 1 2…”

mentioning

confidence: 99%

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Travel preference of bicycle-sharing users: A multi-granularity sequential pattern mining approach

Zhou

Zhang

Kou

et al. 2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Public bicycles are an indispensable part of green public transportation and are also a convenient and economical manner for the general public. In operation management, it is very important and imperative to understand the user demand and pattern of the public bicycle system. This paper took the public bicycle system in Hohhot as the research object, collected nearly 4 years of operating data, and studied the travel preferences of users in the public bicycle system in view of multiple granularities. Specifically, the data of car rental users at three time-granularities were obtained through data extraction technology. Finally, frequent pattern mining was performed on car rental data based on different time granularities and mapped to the user’s riding preference, and then the riding modes of different car rental users founded on different time granularities were determined. Finally, this article gave different management opinions based on the different riding preferences of public bicycle users in Hohhot.

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Section: Analysis Results Of Urpmentioning

confidence: 99%

Section: Proposed Urp Analysis Frameworkmentioning

confidence: 99%

mentioning

confidence: 99%

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Travel preference of bicycle-sharing users: A multi-granularity sequential pattern mining approach

Zhou

Zhang

Kou

et al. 2022

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…The support of individual itemsets is calculated by using Diffsets. Three algorithms namely, fast mining frequent itemsets using Nodesets (FIN), PrePost, and PrePost+ have been customized [19] to extract top rank-k frequent patterns. A dynamic minimum support strategy has been applied to enhance its efficiency.…”

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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“…b) Frequent pattern mining Such patterns are pruned by frequent pattern mining tools, which consider them to be unwanted or of little interest. Because of its usefulness in so many domains, data mining has gotten a lot of interest in the database research community [17]. Fused deposition modeling (FDM) is a widely used data mining technique that is useful for various applications, including association mining, correlations, sequential item-sets, maxitem-sets, partial periodicity, and emergent item-sets.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of frequent pattern mining algorithm on different real-life dataset

Khajuria

Sharma²,

Sharma³

et al. 2023

IJEECS

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<span lang="EN-US">The efficient finding of common patterns: a group of items that appear frequently in a dataset is a critical task in data mining, especially in transaction datasets. The goal of this paper is to look into the efficiency of various algorithms for frequent pattern mining in terms of computing time and memory consumption, as well as the problem of how to apply the algorithms to different datasets. In this paper, the algorithms investigated for mining the frequent patterns are; Pre-post, Pre-post+, FIN, H-mine, R-Elim, and estDec+ algorithms. These algorithms have been implemented and tested on four real-life datasets that are: The retail dataset, the Accidents dataset, the Chess dataset, and the Mushrooms dataset. From the results, it has been observed that, for the Retail dataset, estDec+ algorithm is the fastest among all algorithms in terms of run time as well as consumes less memory for its execution. Pre-post+ algorithm performs better than all other algorithms in terms of run time and maximum memory for the Mushrooms dataset. Pre-Post outperforms other algorithms in terms of performance. And for Accident datasets, in terms of execution time and memory consumption, the FIN method outperforms other algorithms.</span>

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Customized frequent patterns mining algorithms for enhanced Top-Rank-K frequent pattern mining

Cited by 9 publications

References 23 publications

Travel preference of bicycle-sharing users: A multi-granularity sequential pattern mining approach

Travel preference of bicycle-sharing users: A multi-granularity sequential pattern mining approach

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

Performance analysis of frequent pattern mining algorithm on different real-life dataset

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