Association rules are a simple yet powerful tool for making item-based recommendations. As part of the ECML PKDD 2013 Discovery Challenge, we use association rules to form a name recommender system. We introduce a new measure of association rule confidence that incorporates user similarities, and show that this increases prediction performance. With no special feature engineering and no separat...

A discovery method of rules of hierarchy and reliability for very large databases is devised for interactive refinement that can effectively cope with high dimensionality and voluminousness of the data sets. The method takes advantage of the fact that more general concepts occur more frequently and the focus of knowledge discovery is to find some hidden rules that govern some substantial portio...

We investigate new approaches for knowledge discovery from two medical databases. Two different kinds of knowledge, namely rules and causal structures, are learned. Rules capture interesting patterns and regularities in the database. Causal structures represented by Bayesian networks capture the causality relationships among the attributes. We employ advanced evolutionary algorithms for these d...

The discovery of association rules is one of the classic problems of data mining. Typically, it is done over well-structured data, such as databases. In this paper, we present a method of discovery of association rules in semi-structured data, namely, in a set of conceptual graphs. The method is based on conceptual clustering of the data and constructing of a conceptual hierarchy. A feature of ...

The problem of analysis of biological sequences, is the discovery of sequence similarity of various kinds, in the primary structure of related proteins and genes. This sequence search can be applied to various applications like discovery of association rules, strong rules, correlations, sequential rules, frequent episodes, multidimensional patterns and many other important discovery tasks. In t...

An important problem in Linked Data is the discovery of links between entities which identify the same real world object. These links are often generated based on manually written linkage rules which specify the condition which must be fulfilled for two entities in order to be interlinked. In this paper, we present an approach to automatically generate linkage rules from a set of reference link...

Traditional approaches for mining generalized association rules are based only on database contents, and focus on exact matches among items. However, in many applications, the use of some background knowledge, as ontologies, can enhance the discovery process and generate semantically richer rules. In this way, this paper proposes the NARFO algorithm, a new algorithm for mining non-redundant and...

Many studies have investigated the process of rule discovery. However, the data utilized in these studies, such as performance and verbal protocol data, were course-grained. In this study, we designed a new experimental method using eye movement data to observe the detailed process of rule discovery. In the proposed method, we corresponded the task display and a rule space in the participants’ ...

In this paper we study an evolutionary machine learning approach to data mining and knowledge discovery based on the induction of classification rules. A method for automatic rules induction called AREX using evolutionary induction of decision trees and automatic programming is introduced. The proposed algorithm is applied to a cardiovascular dataset consisting of different groups of attributes...

Association rules are ”if-then rules” with two measures which quantify the support and confidence of the rule for a given data set. Having their origin in market basked analysis, association rules are now one of the most popular tools in data mining. This popularity is to a large part due to the availability of efficient algorithms. The first and arguably most influential algorithm for efficien...