Solving the problem of selecting suitable objective measures by clustering association rules through the measures themselves

Many objective measures (OMs) were proposed since they are frequently used to discover interesting association rules. Therefore, an important challenge is to decide which OM to use. For that, one can: (a) reduce the number of OMs to be chosen; (b) aggregate OMs’ values in only one importance value as a mean of not selecting a suitable OM. The problem with (a) is that many OMs can remain. Regarding (b), the problem is that the obtained values cannot be well understandable. This work proposes a process to solve the problem related to the identification of a suitable OM to direct the users towards the interesting patterns. The goal is to find the same interesting patterns, as if the most suitable OM had been used, also trying to reduce the exploration space to minimize the user’s effort.

Keywords

Association rules, Clustering, Objective evaluation measures, Post-processing

Language

English

Citation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9587, p. 505-517.