Automated discovery of Rule is, due to its applicability, one of the most fundamental and important method in KDD. It has been an active research area in the recent past. Hierarchical representation allows us to easily manage the complexity of knowledge, to view the knowledge at different levels of details, and to focus our attention on the interesting aspects only. One of such efficient and ea...

AUTOMATED DATA-DRIVEN DISCOVERY OF MOTIF-BASED PROTEIN FUNCTION CLASSIFIERS Xiangyun Wang, Diane Schroeder, Drena Dobbs, and Vasant Honavar Artificial Intelligence Laboratory Department of Computer Science and Graduate Program in Bioinformatics and Computational Biology Iowa State University Ames, IA 50011, USA www.cs.iastate.edu/~honavar/aigroup.html [email protected] ABSTRACT This paper ...

Data mining methods in software engineering are becoming increasingly important as they can support several aspects of the software development life-cycle such as quality. In this work, we present a data mining approach to induce rules extracted from static software metrics characterising fault-prone modules. Due to the special characteristics of the defect prediction data (imbalanced, inconsis...

By discovering predictive relationships between different pieces of extracted data, data-mining algorithms can be used to improve the accuracy of information extraction. However, textual variation due to typos, abbreviations, and other sources can prevent the productive discovery and utilization of hard-matching rules. Recent methods for inducing softmatching rules from extracted data can more ...

Dieser Artikel zeigt, da Assoziationsregeln fast immer in einem einzigen Scan der Datenbank gefunden werden konnen. Dies wird durch einen randomisierten Ansatz erreicht. Original-Abstract: Discovery of association rules is an important database mining problem. Current algorithms for nding association rules require several passes over the analyzed database, and obviously the role of I/O overhea...

Conditional Functional Dependencies (CFDs) have been proposed as a new type of semantic rules extended from traditional functional dependencies. They have shown great potential for detecting and repairing inconsistent data. Constant CFDs are 100% confidence association rules. The theoretical search space for the minimal set of CFDs is the set of minimal generators and their closures in data. Th...

The World Wide Web is becoming increasingly personalized as users provide more of their information on the Web. Thus, Web service functionality is becoming reliant on user profile information and context in order to provide user-specific data. In this paper, we discuss enhancements to SECE (Sense Everything, Control Everything), a platform for context-aware service composition based on user-def...

We present an elitist multi-objective genetic algorithm (EMOGA) for mining classification rules from large databases. We emphasize on predictive accuracy, comprehensibility and interestingness of the rules. However, predictive accuracy, comprehensibility and interestingness of the rules often conflict with each other. This makes it a multi-objective optimization problem that is very difficult t...

In this chapter we describe how to discover interesting relationships from student’s usage information to improve adaptive web courses. We have used AHA! to make courses that adapt both the presentation and the navigation depending on the level of knowledge that each particular student has. We use data mining methods for providing feedback to courseware authors. The discovered information is pr...

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...