MODENAR

MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules. In this paper, a Pareto-based multi-objective differential evolution (DE) algorithm is proposed as a search strategy for mining accurate and comprehensible numeric association rules (ARs) which are optimal in the wider sense that no other rules are superior to them when all objectives are simultaneously considered. The proposed DE guided the search of ARs toward the global Pareto-optimal set while maintaining adequate population diversity to capture as many high-quality ARs as possible. ARs mining problem is formulated as a four-objective optimization problem. Support, confidence value and the comprehensibility of the rule are maximization objectives while the amplitude of the intervals which conforms the itemset and rule is minimization objective. It has been designed to simultaneously search for intervals of numeric attributes and the discovery of ARs which these intervals conform in only single run of DE. Contrary to the methods used as usual, ARs are directly mined without generating frequent itemsets. The proposed DE performs a database-independent approach which does not rely upon the minimum support and the minimum confidence thresholds which are hard to determine for each database. The efficiency of the proposed DE is validated upon synthetic and real databases.


References in zbMATH (referenced in 11 articles )

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  1. Telikani, Akbar; Gandomi, Amir H.; Shahbahrami, Asadollah: A survey of evolutionary computation for association rule mining (2020)
  2. Dhaenens, Clarisse; Jourdan, Laetitia: Metaheuristics for data mining (2019)
  3. Lin, Qiuzhen; Tang, Chaoyu; Ma, Yueping; Du, Zhihua; Li, Jianqiang; Chen, Jianyong; Ming, Zhong: A novel adaptive control strategy for decomposition-based multiobjective algorithm (2017)
  4. Martín, D.; Rosete, A.; Alcalá-Fdez, J.; Herrera, F.: QAR-CIP-NSGA-II: a new multi-objective evolutionary algorithm to mine quantitative association rules (2014) ioport
  5. Martínez-Ballesteros, M.; Nepomuceno-Chamorro, I. A.; Riquelme, J. C.: Discovering gene association networks by multi-objective evolutionary quantitative association rules (2014)
  6. Minaei-Bidgoli, B.; Barmaki, R.; Nasiri, M.: Mining numerical association rules via multi-objective genetic algorithms (2013) ioport
  7. Gong, Wenyin; Cai, Zhihua; Ling, Charles X.: DE/BBO: a hybrid differential evolution with biogeography-based optimization for global numerical optimization (2011) ioport
  8. Martínez-Ballesteros, M.; Martínez-Álvarez, F.; Troncoso, A.; Riquelme, J. C.: An evolutionary algorithm to discover quantitative association rules in multidimensional time series (2011) ioport
  9. Alatas, Bilal; Akin, Erhan: Rough particle swarm optimization and its applications in data mining (2008)
  10. Alatas, Bilal; Akin, Erhan; Karci, Ali: MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules. (2008) ioport
  11. Gong, Wenyin; Cai, Zhihua; Jiang, Liangxiao: Enhancing the performance of differential evolution using orthogonal design method (2008)