Open AccessThe selection of the relevant association rules using the ELECTRE method with multiple criteria
Author(s) -
Azzeddine Dahbi,
Siham Jabri,
Youssef Balouki,
Taoufiq Gadi
Publication year - 2020
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.341
H-Index - 7
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v9.i4.pp638-645
Subject(s) - electre , computer science , association rule learning , task (project management) , association (psychology) , selection (genetic algorithm) , data mining , measure (data warehouse) , obstacle , compromise , apriori algorithm , multiple criteria decision analysis , operations research , artificial intelligence , mathematics , philosophy , management , epistemology , political science , law , economics , social science , sociology
The extraction of association rules is a very attractive data mining task and the most widespread in the business world and in modern society, trying to obtain the interesting relationship and connection between collections of articles, products or items in high transactional databases. The immense quantity of association rules obtained expresses the main obstacle that a decision maker can handle. Consequently, in order to establish the most interesting association rules, several interestingness measures have been introduced. Currently, there is no optimal measure that can be chosen to judge the selected association rules. To avoid this problem we suggest to apply ELECTRE method one of the multi-criteria decision making, taking into consideration a formal study of measures of interest according to structural properties, and intending to find a good compromise and select the most interesting association rules without eliminating any measures. Experiments conducted on reference data sets show a significant improvement in the performance of the proposed strategy.