NDLI: Association Discovery in Two-View Data

Content Provider	IEEE Xplore Digital Library
Author	van Leeuwen, M. Galbrun, E.
Copyright Year	1989
Abstract	Two-view datasets are datasets whose attributes are naturally split into two sets, each providing a different view on the same set of objects. We introduce the task of finding small and non-redundant sets of associations that describe how the two views are related. To achieve this, we propose a novel approach in which sets of rules are used to translate one view to the other and vice versa. Our models, dubbed translation tables, contain both unidirectional and bidirectional rules that span both views and provide lossless translation from either of the views to the opposite view. To be able to evaluate different translation tables and perform model selection, we present a score based on the Minimum Description Length (MDL) principle. Next, we introduce three TRANSLATOR algorithms to find good models according to this score. The first algorithm is parameter-free and iteratively adds the rule that improves compression most. The other two algorithms use heuristics to achieve better trade-offs between runtime and compression. The empirical evaluation on real-world data demonstrates that only modest numbers of associations are needed to characterize the two-view structure present in the data, while the obtained translation rules are easily interpretable and provide insight into the data.
Sponsorship	IEEE IEEE Comput. Soc. Tech. Committee on Data Eng IEEE Computer Society
Starting Page	3190
Ending Page	3202
Page Count	13
File Size	1541195
File Format	PDF
ISSN	10414347
Volume Number	27
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Itemsets Data models Encoding Association rules Context awareness Data mining Redescription mining Association discovery Two-view data Minimum description length Association rule mining redescription mining two-view data minimum description length association rule mining
Content Type	Text
Resource Type	Article
Subject	Information Systems Computational Theory and Mathematics Computer Science Applications

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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