GRD is an algorithm for k-most interesting rule discovery. In contrast to association rule discovery, GRD does not require the use of a minimum support constraint. Rather, the user must specify a measure of interestingness and the number of rules sought (k). This paper reports efficient techniques to extend GRD to support mining of negative rules.

Local pattern discovery, pattern set discovery and global modeling build together as consecutive steps a specific case of global pattern discovery. As each of these three steps have gained an increased attention in recent years, a great variety of techniques for each step have been proposed. Though so far there has been no systematic comparison of the possible choices. In this paper, we will co...

In spatial data mining, a common task is the discovery of spatial association rules from spatial databases. We propose a distributed system, named ARES that takes advantage of the use of a multi-relational approach to mine spatial association rules. It supports spatial database coupling and discovery of multi-level spatial association rules as a means for spatial data exploration. We also prese...

Automatic discovery of semantic relations between resources is a key issue in Web-based intelligent applications such as document understanding and Web services. This paper explores how to automatically discover the latent semantic relations and their properties based on the existing association rules. Through building semantic matrix by the association rules, four semantic relations can be ext...

We describe an approach and a tool for the discovery of subgroups within the framework of distribution rule mining. Distribution rules are a kind of association rules particularly suited for the exploratory study of numerical variables of interest. Being an exploratory technique, the result of a distribution mining process is typically a very large number of patterns. Exploring such results is ...

Cardiovascular decision support is one area of increasing research interest. On-going collaborations between clinicians and computer scientists are looking at the application of knowledge discovery in databases to the area of patient diagnosis, based on clinical records. A fuzzy rule-based system for risk estimation of cardiovascular patients is proposed. It uses a group of fuzzy rules as a kno...

This paper proposes the automated translation of rules extracted from data mining or knowledge discovery tools into active database rules. We term this process of translating a knowledge discovery rule and incorporating it into a database schema in the form of an ECA (eventcondition-action) rule as database schema refinement. We introduce a new rule identification measure for categorising knowl...

Association rule mining research typically focuses on positive association rules (PARs), generated from frequently occurring itemsets. However, in recent years, there has been a significant research focused on finding interesting infrequent itemsets leading to the discovery of negative association rules (NARs). The discovery of infrequent itemsets is far more difficult than their counterparts, ...

Three new structures in the MDABCO perovskite family of ferroelectrics define design rules for ferroelectric phase discovery.

Discovery of association rules from large databases has been a focused topic recently in the research into database mining. Previous studies discover association rules at a single concept level, however, mining association rules at multiple concept levels may lead to nding more informative and re ned knowledge from data. In this paper, we study e cient methods for mining multiple-level associat...