Decision support based on optimized data mining techniques: Application to mobile telecommunication companies. (22nd June 2020)

• Record Type: Journal Article
• Authors: Berkani, Lamia
• Other Names: Schifanella Claudio guestEditor.; Bracciali Andrea guestEditor.; Benkrid Soumia guestEditor.; Moussa Rim guestEditor.; Badir Hassan guestEditor.; Lo Moussa guestEditor.; Bellatreche Ladjel guestEditor.
• Is Part Of: Concurrency and computation. Volume 33:Number 1(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-22
• Subjects: association rules -- data mining -- decision‐making -- DM and telecommunication -- optimization -- relevance measure; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5833
• Languages: English
• ISSNs: 1532-0626

Abstract

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.