How to efficiently discard potentially uninteresting rules in exploratory rule discovery is one of the important research foci in data mining. Many researchers have presented algorithms to automatically remove potentially uninteresting rules utilizing background knowledge and user-specified constraints. Identifying the significance of exploratory rules using a significance test is desirable for...

In rule learning, rules are typically induced in two phases, rule refinement and rule selection. It was recently argued that the usage of two separate heuristics for each phase—in particular using the so-called inverted heuristic in the refinement phase—produces longer rules with comparable classification accuracy. In this paper we test the utility of inverted heuristics in the context of subgr...

Classification rules and rules describing interesting subgroups are important components of descriptive machine learning. Rule learning algorithms typically proceed in two phases: rule refinement selects conditions for specializing the rule, and rule selection selects the final rule among several rule candidates. While most conventional algorithms use the same heuristic for guiding both phases,...

Due to the vast and rapid increase in the size of data, data mining has been an increasingly important tool for the purpose of knowledge discovery to prevent the presence of rich data but poor knowledge. In this context, machine learning can be seen as a powerful approach to achieve intelligent data mining. In practice, machine learning is also an intelligent approach for predictive modelling. ...

The rule technological landscape is becoming ever more complex, with an extended number of specifications and products. It is therefore becoming increasingly difficult to integrate rule-driven components and manage interoperability in multi-rule engine environments. The described work presents the possibility to provide a common interface for rule-driven components in a distributed system. The ...