Unexpected rules using a conceptual distance based on fuzzy ontology

Journal of King Saud University: Computer and Information Sciences

卷:26

Ramdane Maamri¹  Mohamed Said Hamani²  Yacine Kissoum³  Maamar Sedrati⁴ 

[1] Lire Laboratory, University of Constantine 2, Algeria;

[2] Lire Laboratory, University of M’sila, Algeria;

[3] Lire Laboratory, University of Skikda, Algeria;

[4] University of Hadj Lakhdar Batna, Algeria;

关键词: Fuzzy ontology;    Unexpectedness;    Association rule;    Domain knowledge;    Interestingness;    Conceptual distance;   

DOI  :  10.1016/j.jksuci.2013.06.001

来源: DOAJ

【 摘 要 】

One of the major drawbacks of data mining methods is that they generate a notably large number of rules that are often obvious or useless or, occasionally, out of the user’s interest. To address such drawbacks, we propose in this paper an approach that detects a set of unexpected rules in a discovered association rule set. Generally speaking, the proposed approach investigates the discovered association rules using the user’s domain knowledge, which is represented by a fuzzy domain ontology. Next, we rank the discovered rules according to the conceptual distances of the rules.

【 授权许可】

Unknown