Measuring interesting rules in Characteristic rule

TL;DR

This paper proposes a new method for measuring interesting rules in attribute-oriented induction, focusing on the complexity of concept hierarchies to identify more meaningful generalizations.

Contribution

It introduces an approach that favors simpler concept hierarchies for finding interesting rules, contrasting with previous heuristic methods.

Findings

Simpler concept hierarchies yield more interesting rules.

The approach considers hierarchy complexity in rule interestingness.

It enhances rule selection by evaluating concept tree structure.

Abstract

Finding interesting rule in the sixth strategy step about threshold control on generalized relations in attribute oriented induction, there is possibility to select candidate attribute for further generalization and merging of identical tuples until the number of tuples is no greater than the threshold value, as implemented in basic attribute oriented induction algorithm. At this strategy step there is possibility the number of tuples in final generalization result still greater than threshold value. In order to get the final generalization result which only small number of tuples and can be easy to transfer into simple logical formula, the seventh strategy step about rule transformation is evolved where there will be simplification by unioning or grouping the identical attribute. Our approach to measure interesting rule is opposite with heuristic measurement approach by Fudger and Hamilton where the more complex concept hierarchies, more interesting results are likely to be found, but our approach the simpler concept hierarchies, more interesting results are likely to be found and the more complex concept hierarchies, more complex process generalization in concept tree. The decision to find interesting rule is influenced with wide or length and depth or level of concept tree.