A rough set approach to multiple dataset analysis

In the area of data mining, the discovery of valuable changes and connections (e.g., causality) from multiple data sets has been recognized as an important issue. This issue essentially differs from finding statistical associations in a single data set because it is complicated by the different data...

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Bibliographic Details
Published in:Applied soft computing 2011-03, Vol.11 (2), p.2538-2547
Main Author: Kaneiwa, Ken
Format: Article
Language:English
Subjects:
Causality
Fuzzy theory
Multiple datasets
Rough set theory
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Summary:In the area of data mining, the discovery of valuable changes and connections (e.g., causality) from multiple data sets has been recognized as an important issue. This issue essentially differs from finding statistical associations in a single data set because it is complicated by the different data behaviors and relationships across multiple data sets. Using rough set theory, this paper proposes a change and connection mining algorithm for discovering a time delay between the quantitative changes in the data of two temporal information systems and for generating the association rules of changes from their connected decision table. We establish evaluation criteria for the connectedness of two temporal information systems with varying time delays by calculating weight-based accuracy and coverage of the association rules of changes, adjusted by a fuzzy membership function.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2010.08.021