A new multiple seeds based genetic algorithm for discovering a set of interesting Boolean association rules

Kabir, Mir Md. Jahangir; Xu, Shuxiang; Kang, Byeong Ho; Zhao, Zongyuan

doi:10.1016/j.eswa.2017.01.001

Cited by 36 publications

(17 citation statements)

References 20 publications

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“…Chromosomes are reproduced by the crossover and the mutation and create a new generation. There are many methods how to select the best chromosomes, for example roulette wheel selection, tournament selection, rank selection and some others [20][21][22].…”

Section: B Genetic Algorithm (Ga)mentioning

confidence: 99%

Improve Performance of Association Rule-Based Collaborative Filtering Recommendation Systems using Genetic Algorithm

Neysiani¹,

Soltani²,

Mofidi³

et al. 2019

IJITCS

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Recommender systems that possess adequate information about users and analyze their information, are capable of offering appropriate items to customers. Collaborative filtering method is one of the popular recommender system approaches that produces the best suggestions by identifying similar users or items based on their previous transactions. The low accuracy of suggestions is one of the major concerns in the collaborative filtering method. Several methods have been introduced to enhance the accuracy of this method through the discovering association rules and using evolutionary algorithms such as particle swarm optimization. However, their runtime performance does not satisfy this need, thus this article proposes an efficient method of producing cred associations rules with higher performances based on a genetic algorithm. Evaluations were performed on the data set of MovieLens. The parameters of the assessment are: run time, the average of quality rules, recall, precision, accuracy and F1-measurement. The experimental evaluation of a system based on our algorithm outperforms show than the performance of the multiobjective particle swarm optimization association rule mining algorithm, finally runtime has dropped by around 10%.

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Section: B Genetic Algorithm (Ga)mentioning

confidence: 99%

Improve Performance of Association Rule-Based Collaborative Filtering Recommendation Systems using Genetic Algorithm

Neysiani¹,

Soltani²,

Mofidi³

et al. 2019

IJITCS

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“…Vertical data is more efficient than horizontal data in the process of obtaining the support of items because an algorithm only needs to read the columns related to a query, but does not need to read other unnecessary columns. For instance, if the support of itemset {I 1 I 2 } is needed in Table 1, an algorithm just needs to read and intersect the TID_sets of I 1 and I 2 and find support (I 1 I 2 ) = Num[ (1,4,5,7,8,9)∩ (1,2,3,4,6,8,9)] = Num(1, 4, 8, 9) = 4, instead of scanning the entire database as using horizontal data. Table 1.…”

Section: The Eclat Algorithmmentioning

confidence: 99%

“…Data mining is a knowledge discovery process from large amounts of data and has been extensively studied in many fields. Frequent pattern mining is a fundamental field of data mining, and the goal is to find patterns that appear frequently in a database [8][9][10][11]. Many algorithms for mining frequent patterns, such as Apriori, FP-growth, and Eclat, to mention only a few, have been developed.…”

Section: Introductionmentioning

confidence: 99%

An Improved Eclat Algorithm Based on Tissue-Like P System with Active Membranes

2019

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The Eclat algorithm is a typical frequent pattern mining algorithm using vertical data. This study proposes an improved Eclat algorithm called ETPAM, based on the tissue-like P system with active membranes. The active membranes are used to run evolution rules, i.e., object rewriting rules, in parallel. Moreover, ETPAM utilizes subsume indices and an early pruning strategy to reduce the number of frequent pattern candidates and subsumes. The time complexity of ETPAM is decreased from O(t2) to O(t) as compared with the original Eclat algorithm through the parallelism of the P system. The experimental results using two databases indicate that ETPAM performs very well in mining frequent patterns, and the experimental results using four databases prove that ETPAM is computationally very efficient as compared with three other existing frequent pattern mining algorithms.

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“…Confidence [3]: Another interestingness measure is used to evaluate an association rule, named confidence. It gives the probability of a rule of checking conditions of the consequent for a transaction which contains the antecedent [8]. The ranges of a confidence varies from 0 to 1.…”

Section: Interestingness Measuresmentioning

confidence: 99%

Regression and Correlation Analysis of Different Interesting Measures for Mining Association Rules

Kabir¹,

Anzar²

2018

IJIRCST

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Association Rule Mining is the significant way to extract knowledge from data sets. The association among the instance of a dataset can measured with Interestingness Measures (IM) metrics. IM define how much interesting the extract knowledge is. Researchers have proved that the classical Support-Confidence metrics can't extract the real knowledge and they have been proposing different IM. From a user perspective it's really tough to select the minimal and best measures from them. From our experiment, the correlation among the various IM such as Support, Confidence, Lift, Cosine, Jaccard, Leverage etc. are evaluated in different popular data sets. In this paper our contribution is to find the correlation among the IM with different ranges in different types of data sets which were not applied in past researches. This study also identified that the correlation varies from data set to data set and proposed a solution based on multiple criterion that will help the users to select the minimal and best from a large number of IM.

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A new multiple seeds based genetic algorithm for discovering a set of interesting Boolean association rules

Cited by 36 publications

References 20 publications

Improve Performance of Association Rule-Based Collaborative Filtering Recommendation Systems using Genetic Algorithm

Improve Performance of Association Rule-Based Collaborative Filtering Recommendation Systems using Genetic Algorithm

An Improved Eclat Algorithm Based on Tissue-Like P System with Active Membranes

Regression and Correlation Analysis of Different Interesting Measures for Mining Association Rules

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