An important issue in data mining is scalability with respect to the size of the dataset being mined. In the paper we address this issue by presenting a parallel GA for rule discovery. This algorithm exploits both data parallelism, by distributing the data being mined across all available processors, and control parallelism, by distributing the population of individuals across all available pro...

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’ ...

This paper investigates how to adapt standard classification rule learning approaches to subgroup discovery. The goal of subgroup discovery is to find rules describing subsets of the population that are sufficiently large and statistically unusual. The paper presents a subgroup discovery algorithm, CN2-SD, developed by modifying parts of the CN2 classification rule learner: its covering algorit...

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...

Data-intensive software programs typically transform a set of source data values into target data values. While the group of developers who write, compile and test the software or the query have a clear understanding of the transformation logic (or transformation rules) at the time the program is created, that understanding can quickly fade at an enterprise level for a variety of reasons, inclu...

Data is not the only resource that can lead to knowledge discovery. Knowledge itself in the form of an expert system or output from data mining can be further manipulated to produce multi-level knowledge. This can be used to suggest more interesting concepts and improve understanding and querying of the original data and/or knowledge. Knowledge has different properties to data, the most obvious...

The Apriori algorithm’s frequent itemset approach has become the standard approach to discovering association rules. However, the computation requirements of the frequent itemset approach are infeasible for dense data and the approach is unable to discover infrequent associations. OPUS AR is an efficient algorithm for association rule discovery that does not utilize frequent itemsets and hence ...