A multiobjective evolution algorithm based rule certainty updating strategy in big data environment
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| Title: | A multiobjective evolution algorithm based rule certainty updating strategy in big data environment |
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| Authors: | J Mi, K Wang, B Liu, F Ding, Y Sun, H Huang |
| Publication Year: | 2018 |
| Subject Terms: | Artificial intelligence not elsewhere classified, Distributed computing and systems software not elsewhere classified, Information systems not elsewhere classified, Big Data, Association Rules Mining, Multiobjective Evolution Algorithm, School of Information Technology, 4602 Artificial intelligence, 4605 Data management and data science |
| Description: | With the ubiquitous deployment of the mobile devices and the explosive growth of Internet traffic, an emerging method called association rules mining (ARM) is proposed to solve the problem of mining potential value of existing big data. However, massive ARM methods focus on positive rules which are easy to ignore interesting information because of negative ones. This paper studies a practical problem of combing negative rules in ARM research. Specifically, we propose a rule certainty updating strategy (RCUS) to combine positive rules with negative rules, which consists of two parts: initialization and updating. To solve the large scale problem with negative rules, the proposed strategy decomposes the large scale problem into several relatively small ones by an improved multiobjective evolutionary algorithm (MOEA) with gene representation and certainty. Simulation results show that our method is outstanding when the scale of attributes and examples is increasing. |
| Document Type: | conference object |
| Language: | unknown |
| Relation: | http://hdl.handle.net/10536/DRO/DU:30118867; https://figshare.com/articles/conference_contribution/A_multiobjective_evolution_algorithm_based_rule_certainty_updating_strategy_in_big_data_environment/20802754 |
| Availability: | http://hdl.handle.net/10536/DRO/DU:30118867 https://figshare.com/articles/conference_contribution/A_multiobjective_evolution_algorithm_based_rule_certainty_updating_strategy_in_big_data_environment/20802754 |
| Rights: | All Rights Reserved |
| Accession Number: | edsbas.B6FD584E |
| Database: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | With the ubiquitous deployment of the mobile devices and the explosive growth of Internet traffic, an emerging method called association rules mining (ARM) is proposed to solve the problem of mining potential value of existing big data. However, massive ARM methods focus on positive rules which are easy to ignore interesting information because of negative ones. This paper studies a practical problem of combing negative rules in ARM research. Specifically, we propose a rule certainty updating strategy (RCUS) to combine positive rules with negative rules, which consists of two parts: initialization and updating. To solve the large scale problem with negative rules, the proposed strategy decomposes the large scale problem into several relatively small ones by an improved multiobjective evolutionary algorithm (MOEA) with gene representation and certainty. Simulation results show that our method is outstanding when the scale of attributes and examples is increasing. |
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