International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Knowledge discovery in databases: an overview
AI Magazine
Machine Learning
Learning Classification Rules Using Lattices (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
A User-Driven Process for Mining Association Rules
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Pre-pruning Classification Trees to Reduce Overfitting in Noisy Domains
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
A Rough Set Framework for Data Mining of Propositional Default Rules
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Interactive classification using a granule network
ICCI '05 Proceedings of the Fourth IEEE International Conference on Cognitive Informatics
A Systemic Framework for the Field of Data Mining and Knowledge Discovery
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Domain-Driven Data Mining: Methodologies and Applications
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
An analysis of reduced error pruning
Journal of Artificial Intelligence Research
Research on system uncertainty measures based on rough set theory
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
User-driven fuzzy clustering: on the road to semantic classification
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Incremental classification rules based on association rules using formal concept analysis
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
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Recent developments in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for knowledge discovery large-scale database. Data mining technology is a useful tool for this task. It is an emerging area of computational intelligence that offers new theories, techniques, and tools for processing large volumes of data, such as data analysis, decision making, etc. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Unfortunately,most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we would obey in a data mining process? What is the relationship between the prior knowledge of domain experts and the knowledgemind from data? In this paper, we will address these basic issues of data mining from the viewpoint of informatics [1]. Data is taken as a manmade format for encoding knowledge about the natural world. We take data mining as a process of knowledge transformation. A domain-oriented data-driven data mining (3DM) model based on a conceptual data mining model is proposed. Some data-driven data mining algorithms are also proposed to show the validity of this model, e.g., the data-driven default rule generation algorithm, data-driven decision tree pre-pruning algorithm and data-driven knowledge acquisition from concept lattice.