Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Neighborhood systems and relational databases
CSC '88 Proceedings of the 1988 ACM sixteenth annual conference on Computer science
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Topological data models and approximate retrieval and reasoning
CSC '89 Proceedings of the 17th conference on ACM Annual Computer Science Conference
On modal and fuzzy decision logics based on rough set theory
Fundamenta Informaticae
Association Rules in Semantically Rich Relations: Granular Computing Approach
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Finding Association Rules Using Fast Bit Computation: Machine-Oriented Modeling
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
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
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Granulation and nearest neighborhoods: rough set approach
Granular computing
Data mining using granular computing: fast algorithms for finding association rules
Data mining, rough sets and granular computing
Mining association rules from imprecise ordinal data
Fuzzy Sets and Systems
A theoretical investigation of regular equivalences for fuzzy graphs
International Journal of Approximate Reasoning
SVMs modeling for highly imbalanced classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Mining association rules with multiple minimum supports using maximum constraints
International Journal of Approximate Reasoning
Granular computing: structures, representations, and applications
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
FL-GrCCA: A granular computing classification algorithm based on fuzzy lattices
Computers & Mathematics with Applications
Sparse nonnegative matrix factorization for protein sequence motif discovery
Expert Systems with Applications: An International Journal
A roadmap from rough set theory to granular computing
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Theoretical study of granular computing
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Attribute reduction based on granular computing
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Table representations of granulations revisited
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Rough sets and decision rules in fuzzy set-valued information systems
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
An efficient approach to mine rare association rules using maximum items' support constraints
BNCOD'10 Proceedings of the 27th British national conference on Data Security and Security Data
On Modal and Fuzzy Decision Logics Based on Rough Set Theory
Fundamenta Informaticae
Information Sciences: an International Journal
Set-based granular computing: A lattice model
International Journal of Approximate Reasoning
Unifying Rough Set Theories via Large Scaled Granular Computing
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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From the processing point of view, data mining is machine derivation of interesting properties (to human) from the stored data. Hence, the notion of machine oriented data modeling is explored: An attribute value, in a relational model, is a meaningful label (a property) of a set of entities (granule). A model using these granules themselves as attribute values (their bit patterns or lists of members) is called a machine oriented data model. The model provides a good database compaction and data mining environment. For moderate size databases, finding association rules, decision rules, and etc., can be reduced to easy computation of iset theoretical operations of granules. In the second part, these notions are extended to real world objects, where the universe is granulated (clustered) into granules by binary relations. Data modeling and mining with such additional semantics are formulated and investigated. In such models, data mining is essentially a machine “calculus” of granules-granular computing.