Unexpected rules using a conceptual distance based on fuzzy ontology
Mohamed Said Hamani, Ramdane Maamri, Yacine Kissoum, Maamar Sedrati · Journal of King Saud University - Computer and Information Sciences · 2013
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.