Multi-objective PSO algorithm for mining numerical association rules without a priori discretization |
| |
| Affiliation: | 1. IGS (Optimization), School of Arts and Sciences, The University of British Columbia, Canada;2. Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, University Kebangsaan Malaysia, 63400 Bangi, Selangor, Malaysia;1. Department of Software Design and Management, Gachon University, Republic of Korea;2. Department of Computer Science, University of Minnesota, United States;3. NHN Institute for The Next Network, Republic of Korea;4. Naver Corporation, Republic of Korea;1. School of Telecommunication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, PR China;2. School of Computer Science, Shaanxi Normal University, Xi’an, PR China;1. Graduate Program in Computer Science, PPGI, UFES Federal University of Espirito Santo, Av. Fernando Ferrari, 514, CEP 29075-910 Vitória, Espírito Santo, ES, Brazil;2. Department of Production Engineering & Graduate Program in Computer Science, PPGI, UFES Federal University of Espirito Santo, Av. Fernando Ferrari, 514, CEP 29075-910 Vitória, Espírito Santo, ES, Brazil;1. Information Technology Research Group (GTI), Universidad del Cauca, Sector Tulcán Office 450, Popayán, Colombia;2. Computer Science Department, Electronic and Telecommunications Engineering Faculty, Universidad del Cauca, Colombia;3. Data Mining Research Group (MIDAS), Engineering Faculty, Universidad Nacional de Colombia, Bogotá, Colombia |
| |
| Abstract: | In the domain of association rules mining (ARM) discovering the rules for numerical attributes is still a challenging issue. Most of the popular approaches for numerical ARM require a priori data discretization to handle the numerical attributes. Moreover, in the process of discovering relations among data, often more than one objective (quality measure) is required, and in most cases, such objectives include conflicting measures. In such a situation, it is recommended to obtain the optimal trade-off between objectives. This paper deals with the numerical ARM problem using a multi-objective perspective by proposing a multi-objective particle swarm optimization algorithm (i.e., MOPAR) for numerical ARM that discovers numerical association rules (ARs) in only one single step. To identify more efficient ARs, several objectives are defined in the proposed multi-objective optimization approach, including confidence, comprehensibility, and interestingness. Finally, by using the Pareto optimality the best ARs are extracted. To deal with numerical attributes, we use rough values containing lower and upper bounds to show the intervals of attributes. In the experimental section of the paper, we analyze the effect of operators used in this study, compare our method to the most popular evolutionary-based proposals for ARM and present an analysis of the mined ARs. The results show that MOPAR extracts reliable (with confidence values close to 95%), comprehensible, and interesting numerical ARs when attaining the optimal trade-off between confidence, comprehensibility and interestingness. |
| |
| Keywords: | Data mining Multi-objective optimization Association rules Evolutionary algorithms |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
