Data mining using granular computing: fast algorithms for finding association rules

Authors:
T. Y. Lin;Eric Louie
Affiliations:
Department of Mathematics and Computer Science, San Jose State University, San Jose, California and Berkeley Initiative in Soft Computing, University of California, Berkeley, California;IBM Almaden Research Center, 650 Harry Road, San Jose, CA
Venue:
Data mining, rough sets and granular computing
Year:
2002

Citing 10
Cited 4

An introduction to database systems: vol. 1 (5th ed.)

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SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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Journal of Computer and System Sciences
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
Data Mining and Machine Oriented Modeling: A Granular Computing Approach

Applied Intelligence
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Finding Association Rules Using Fast Bit Computation: Machine-Oriented Modeling

ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Granular Computing on Binary Relations

TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Theory of Relational Databases

Theory of Relational Databases

The Study of Some Important Theoretical Problems for Rough Relational Database

RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
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RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
The measures relationships study of three soft rules based on granular computing

RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
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AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence

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Abstract

An attribute value of a relation is a meaningful name(common property) of a group of entities (elementary granule). A relational model using such elementary granules as its attribute values is called machine oriented relational model. In such a model, data processing, in particular finding association rules is transformed into granular computing. In this paper, algorithms for finding association rules by granular computing is presented. Analysis and experiments show that the computation is fast and is a promising approach. Experiments show about 15-20 time faster; theoretical analysis indicates that on the counting the support step, which is the major step, it is at least 32 (wordsize) time faster.