Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Data Mining and Machine Oriented Modeling: A Granular Computing Approach
Applied Intelligence
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Theory of Relational Databases
Theory of Relational Databases
Association Rules in Semantically Rich Relations: Granular Computing Approach
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Database Mining on Derived Attributes
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Generating Concept Hierarchies/Networks: Mining Additional Semantics in Relational Data
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Data mining using granular computing: fast algorithms for finding association rules
Data mining, rough sets and granular computing
Attribute reduction based on granular computing
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Efficient colossal pattern mining in high dimensional datasets
Knowledge-Based Systems
A formal model for mining fuzzy rules using the RL representation theory
Information Sciences: an International Journal
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This paper continue the study of machine oriented models initiated by the second author. An attribute value is regarded as a name of the collection (called granule) of the entities that have the same property (specified by the attribute value). The relational model uses these granules (e.g., bit representation of subsets) as attribute values is called machine oriented data model. The model transforms data mining, particularly finding association rules, into Boolean operations. This paper show that this approach speed up data mining process tremendously; in the experiments, it is approximately 50 times faster, the pre-processing time was included).