Analytical Study of Association Rule Mining Algorithm for Retrieving Frequent Itemsets in Big Datasets

Pandey, Sachin Kumar

doi:10.26438/ijcse/v6i7.424436

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“…42 Several research works adopted the Hadoop with MapReduce programming engine for frequent itemset mining on big data. [43][44][45] Finally, on a practical level, the deployment of our solution in a real setting within the CRM service, would allow us to see the contribution of "optimized" ARs compared to other rules and to show the advantage of using our framework in the decision-making process. A qualitative and quantitative evaluation would provide a better insight on the user feedback (the company's decision-makers).…”

Section: Discussionmentioning

confidence: 99%

Decision support based on optimized data mining techniques: Application to mobile telecommunication companies

Berkani

2020

Concurrency and Computation

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Summary One of the most important challenges of African countries is the effective use of information and communication technologies and its generalization in the different sectors (education, economic, political, and so on). This will have a great impact on different aspects of society and economic activities by making everyday procedures easier and more efficient. In this same context, we are interested in this article by the proposition of a decision support framework based on the use of data mining (DM) techniques. Using DM in e‐government is the process of translating data to appropriate knowledge which can be useful for decision‐making. We propose a framework whose goal is the generation of association rules (ARs) for better decision‐making. This framework includes two approaches: (1) the first approach applies different DM algorithms and (2) the second approach optimizes the first one by considering two different metaheuristics: the Genetic algorithm and the Cuckoo search algorithm. A new relevance measure called “Weighted Dominance” has been considered to evaluate the quality of the generated ARs. Extensive experiments have been conducted using different datasets. The results obtained demonstrated the effectiveness of combining DM and optimization algorithms compared to the first approach. Finally, a case study related to an Algerian mobile phone company has been presented illustrating the use of our framework in the decision‐making process.

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Section: Discussionmentioning

confidence: 99%

Decision support based on optimized data mining techniques: Application to mobile telecommunication companies

Berkani

2020

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

Analytical Study of Association Rule Mining Algorithm for Retrieving Frequent Itemsets in Big Datasets

Cited by 1 publication

References 0 publications

Decision support based on optimized data mining techniques: Application to mobile telecommunication companies

Decision support based on optimized data mining techniques: Application to mobile telecommunication companies

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