A horizontal partitioning‐based method for frequent pattern mining in transport timetable
Author:
Affiliation:
1. CEFET/RJ ‐ Federal Center for Technological Education of Rio de Janeiro Rio de Janeiro Brazil
2. PSI Technologies Saskatchewan Canada
3. COPPE/UFRJ, Federal University of Rio de Janeiro Rio de Janeiro Brazil
Funder
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
Publisher
Wiley
Subject
Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.12881
Reference36 articles.
1. Timetable Generation
2. Using Scalable Data Mining for Predicting Flight Delays
3. Biswas S. Biswas N. &Mondal K.(2018).Parallel apriori based distributed association rule mining: A comprehensive survey.Proceedings ‐ 2018 4th IEEE International Conference on Research in Computational Intelligence and Communication Networks ICRCICN 2018(pp. 202–207).
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