Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Information Sciences—Informatics and Computer Science: An International Journal
Efficient Rule-Based Attribute-Oriented Induction for Data Mining
Journal of Intelligent Information Systems
Knowledge Discovery and Measures of Interest
Knowledge Discovery and Measures of Interest
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Efficient Attribute-Oriented Generalization for Knowledge Discovery from Large Databases
IEEE Transactions on Knowledge and Data Engineering
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
SAINTETIQ: a fuzzy set-based approach to database summarization
Fuzzy Sets and Systems - Data bases and approximate reasoning
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
An Attribute-Oriented Approach for Learning Classification Rules from Relational Databases
Proceedings of the Sixth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Extending Attribute-Oriented Induction as a Key-Preserving Data Mining Method
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 6 - Volume 6
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Use of data mining techniques to model crime scene investigator performance
Knowledge-Based Systems
Spatially enabled customer segmentation using a data classification method with uncertain predicates
Decision Support Systems
RMAIN: Association rules maintenance without reruns through data
Information Sciences: an International Journal
Enabling customer relationship management in ISP services through mining usage patterns
Expert Systems with Applications: An International Journal
Web user behavioral profiling for user identification
Decision Support Systems
Data mining for credit card fraud: A comparative study
Decision Support Systems
Database summarization using fuzzy ISA hierarchies
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid heuristic approach for attribute-oriented mining
Decision Support Systems
Knowledge reduction for decision tables with attribute value taxonomies
Knowledge-Based Systems
Multi-level rough set reduction for decision rule mining
Applied Intelligence
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The attribute-oriented induction (AOI) is a useful data mining method that extracts generalized knowledge from relational data and user's background knowledge. The method uses two thresholds, the relation threshold and attribute threshold, to guide the generalization process, and output generalized knowledge, a set of generalized tuples which describes the major characteristics of the target relation. Although AOI has been widely used in various applications, a potential weakness of this method is that it only provides a snapshot of the generalized knowledge, not a global picture. When thresholds are different, we would obtain different sets of generalized tuples, which also describe the major characteristics of the target relation. If a user wants to ascertain a global picture of induction, he or she must try different thresholds repeatedly. That is time-consuming and tedious. In this study, we propose a global AOI (GAOI) method, which employs the multiple-level mining technique with multiple minimum supports to generate all interesting generalized knowledge at one time. Experiment results on real-life dataset show that the proposed method is effective in finding global generalized knowledge.