Navigating through multidimensional data cubes is a nontrivial task. Although On-Line Analytical Processing (OLAP) provides the capability to view multidimensional data through rollup, drill-down, and slicing-dicing, it offers minimal guidance to end users in the actual knowledge discovery process. In this article, we address this knowledge discovery problem by identifying novel and useful patt...

In addition to sharing and applying the knowledge in the community, knowledge discovery has become an important issue in the knowledge economic era. Data mining plays an important role of knowledge discovery. Therefore, this study intends to propose a novel framework of data mining which clusters the data first and then followed by association rules mining. The first stage employs the ant syste...

Attribute-oriented induction approach (AOA) has been developed for knowledge discovery in large relational database. Several kinds of knowledge, such as characteristic rules and discrimination or classification rules can be discovered. These rules may contain unnecessary conditions and/or unnecessary conditionvalues. A Tuple-oriented approach (TOA) examines one tuple at a time since there are l...

Incompleteness of datasets is one of the important issues in the area of knowledge discovery in medicine. This study proposes a rough set theory (RST)-based knowledge discovery from coronary artery disease (CAD) datasets when there are only small number of objects and contain missing data (incomplete). At first, RST combined with artificial neural network (ANN) is developed to impute the missin...

Motif discovery in biological sequences is an important field in bioinformatics. Most of the scientific research focuses on the de novo discovery of single motifs, but biological activities are typically co-regulated by several factors and this feature is properly reflected by higher order structures, called composite motifs, or cis-regulatory modules or simply modules. A module is a set of mot...

Navigating through multidimensional data cubes is a non-trivial task. Although On-Line Analytical Processing (OLAP) provides the capability to view multidimensional data in different perspectives through roll-up, drill-down, and slicing-dicing, it offers only minimal guidance to end users in the actual knowledge discovery process. It is impractical to navigate through the enormous numbers of cu...

We propose an adaptive, sequential methodology for testing multiple hypotheses. Our methodology consists of a new criterion, the excess discovery count (EDC), and a new class of testing procedures that we call alpha-investing rules. The excess discovery count is the difference between the number of correctly rejected null hypotheses and a fraction of the total number of rejected hypotheses. EDC...

Animals are able to discover the minimal number of actions that achieves an outcome (the minimal action sequence). In most accounts of this, actions are associated with a measure of behavior that is higher for actions that lead to the outcome with a shorter action sequence, and learning mechanisms find the actions associated with the highest measure. In this sense, previous accounts focus on mo...

In this paper, we present an algorithm that discovers action rules from a decision table. Action rules describe possible transitions of objects from one state to another with respect to a distinguished attribute. The previous research on action rule discovery required the extraction of classification rules before constructing any action rule. The new proposed algorithm does not require preexist...