This paper shows preliminary results, on financial data, of an algorithm for discovering pairs of an exception rule and a common sense rule under a prespecified schedule. An exception rule, which represents a regularity of exceptions to a common sense rule, often exhibits interestingness. Discovery of pairs of an exception rule and a common sense rule under threshold scheduling has been success...

Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, r...

In the 2-4-6 rule discovery task, reasoners seek to discover a rule that governs the arrangement of three numbers (or triple). The to-be-discovered rule is ‘any increasing sequence’. Upon being given the triple 2-4-6 as an initial example, however, reasoners are lured to formulate overly specific hypotheses. Traditionally, this task is conducted primarily from an internal representation of the ...

Because exploratory rule discovery works with data that is only a sample of the phenomena to be investigated, some resulting rules may appear interesting only by chance. Techniques are developed for automatically discarding statistically insignificant exploratory rules that cannot survive a hypothesis with regard to its ancestors. We call such insignificant rules derivative extended rules. In t...

Introduction The main objective of the RataMine is to provide ap plication development interface to develop knowledge discovery applications on the top of large databases. Current database systems have been designed mainly to support business applications. The success of SQL capitalized on a small number of primitives which are sufficient to support a vast majority of ap plications today. Unfor...

& This paper presents a subgroup discovery algorithm APRIORI-SD, developed by adapting association rule learning to subgroup discovery. The paper contributes to subgroup discovery, to a better understanding of the weighted covering algorithm, and the properties of the weighted relative accuracy heuristic by analyzing their performance in the ROC space. An experimental comparison with rule learn...

A classifier system is a machine learning system that learns a collection of rules, called classifiers. Mostly, classifiers can be regarded as simple stimulus-response rules. A first level of learning called credit assignment level, consists of reinforcement learning on these classifiers. A classifier is reinforced in dependence on the result of an interaction between the CS and its environment...

Relational rule learning is typically used in solving classification and prediction tasks. However, it can also be adapted to the description task of subgroup discovery. This paper takes a propositionalization approach to relational subgroup discovery (RSD), based on adapting rule learning and first-order feature construction, applicable in individualcentered domains. It focuses on the use of c...

This paper proposes Particle Swarm Optimization (PSO) algorithm to discover classification rules. The potential IF-THEN rules are encoded into real-valued particles that contain all types of attributes in data sets. Rule discovery task is formulized into an optimization problem with the objective to get the high accuracy, generalization performance, and comprehensibility, and then PSO algorithm...

Decision tree induction algorithms generally adopt a greedy approach to select attributes in order to optimize some criteria at each iteration of the tree induction process. When a decision tree has been constructed, a set of decision rules may be correspondingly derived. Univariate decision tree induction algorithms generally yield the same tree regardless of how many times it is induced from ...