Machine Learning
A Comparison of Attribute Selection Strategies for Attribute-Oriented Generalization
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
A Heuristic for Evaluating Databases for Knowledge Discovery with DBLEARN
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
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
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Attribute-Oriented Induction (AOI) is a data mining technique that produces simplified descriptive patterns. Classical AOI uses a predictive strategy to determine distinct values of an attribute but generalises attributes indiscriminately i.e. the value 'ANY' is replaced like any other value without restrictions. AOI only produces interesting rules by using interior concepts of attribute hierarchies. The COMPARE algorithm that integrates predictive and lookahead methods and of order complexity O(np), where n and p are input and generalised tuples respectively, is introduced. The latter method determines distinct values of attribute clusters and greatest number of attribute values with a 'common parent' (except parent 'ANY'). When generating rules, a rough set approach to eliminate redundant attributes is used leading to more interesting multiple-level rules with fewer 'ANY' values than classical AOI.