Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
484028 | Journal of King Saud University - Computer and Information Sciences | 2014 | 11 Pages |
Abstract
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Mohamed Said Hamani, Ramdane Maamri, Yacine Kissoum, Maamar Sedrati,