An efficient measure for evaluating association rules

Author

Djenouri, Youcef ; Gheraibia, Youcef ; Mehdi, Malika ; Bendjoudi, Ahcene ; Nouali-Taboudjemat, Nadia

Author_Institution

Algeria Dept. Math & Comput. Sci., Univ. Souk Ahras, Algiers, Algeria

fYear

2014

fDate

11-14 Aug. 2014

Firstpage

406

Lastpage

410

Abstract

Association rules mining (ARM) has attracted a lot of attention in the last decade. It aims to extract a set of relevant rules from a given database. In order to evaluate the quality of the resulting rules, existing measures, such as support and confidence, allow to evaluate the resulted rules of ARM process separately, missing the different dependencies between the rules. This paper addresses the problem of evaluating rules by taking into account two aspects: (1) The accuracy of the returned rules on the input data and (2) the distance between the returned rules. The rules set that covers the maximum of rules space is considered. To analyze the behavior of the proposed measure, it has been tested on two recent ARM algorithms BSO-ARM and HBSO-TS.

Keywords

data mining; ARM process; BSO-ARM; HBSO-TS; association rules mining; returned rules accuracy; rules evaluation; rules space; Association rules; Frequency measurement; Genetic algorithms; Itemsets; Pollution measurement; Association rules mining; Evaluation of Rules; Rules Quality;

fLanguage

English

Publisher

ieee

Conference_Titel

Soft Computing and Pattern Recognition (SoCPaR), 2014 6th International Conference of

Conference_Location

Tunis

Type

conf

DOI

10.1109/SOCPAR.2014.7008041

Filename

7008041