A data envelopment model for aggregating preference rankings
Management Science
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
Finding the most efficient DMUs in DEA: An improved integrated model
Computers and Industrial Engineering
Ranking discovered rules from data mining with multiple criteria by data envelopment analysis
Expert Systems with Applications: An International Journal
A fuzzy DEA/AR approach to the selection of flexible manufacturing systems
Computers and Industrial Engineering
Efficiency evaluation of data warehouse operations
Decision Support Systems
Prioritization of association rules in data mining: Multiple criteria decision approach
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
An evaluation of heuristics for rule ranking
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Optimonotone Measures For Optimal Rule Discovery
Computational Intelligence
Association Rules Evaluation by a Hybrid Multiple Criteria Decision Method
International Journal of Knowledge and Systems Science
Fuzzy association rule mining approaches for enhancing prediction performance
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Data mining techniques, extracting patterns from large databases have become widespread in business. Using these techniques, various rules may be obtained and only a small number of these rules may be selected for implementation due, at least in part, to limitations of budget and resources. Evaluating and ranking the interestingness or usefulness of association rules is important in data mining. This paper proposes a new integrated data envelopment analysis (DEA) model which is able to find most efficient association rule by solving only one mixed integer linear programming (MILP). Then, utilizing this model, a new method for prioritizing association rules by considering multiple criteria is proposed. As an advantage, the proposed method is computationally more efficient than previous works. Using an example of market basket analysis, applicability of our DEA based method for measuring the efficiency of association rules with multiple criteria is illustrated.