From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
Graphical models for discovering knowledge
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in knowledge discovery and data mining
Latticial structures in data analysis
Theoretical Computer Science
A genetic algorithm for visualizing networks of association rules
IEA/AIE '99 Proceedings of the 12th international conference on Industrial and engineering applications of artificial intelligence and expert systems: multiple approaches to intelligent systems
Visualization Techniques for Mining Large Databases: A Comparison
IEEE Transactions on Knowledge and Data Engineering
Improving the Discovery of Association Rules with Intensity of Implication
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Rule Evaluations in a KDD System
DEXA '95 Proceedings of the 6th International Conference on Database and Expert Systems Applications
A User-driven and Quality-oriented Visualization for Mining Association Rules
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
3DM: Domain-oriented Data-driven Data Mining
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
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This paper describes the components of a human-centered process for discovering association rules where the user is considered as a heuristic which drives the mining algorithms via a well-adapted interface. In this approach, inspired by experimental works on behaviors during a discovery stage, the rule extraction is dynamic : at each step, the user can focus on a subset of potentially interesting items and launch an algorithm for extracting the relevant associated rules according to statistical measures. The discovered rules are represented by a graph updated at each step, and the mining algorithm is an adaptation of the well-known A Priori algorithm where rules are computed locally. Experimental results on a real corpus built from marketing data illustrate the different steps of this process.