A Lossless Representation for Association Rules Satisfying Multiple Evaluation Criteria

Marzena Kryszkiewicz


A lot of data mining literature is devoted to association rules and their evaluation. As the number of discovered rules is often huge, their direct usage by a human being may be infeasible. In the case of classical strong association rules, which are defined as rules supported by sufficiently large fraction of data and having sufficient confidence, a number of their concise representations have been proposed. However, as indicated in the literature, support and confidence measures of association rules seem not to cover many aspects that could be of interest to a user. In consequence, many other measures have been proposed to evaluate association rules. In this paper, we identify an important and wide class of rule ACBC-evaluation measures and offer a lossless representation of association rules satisfying constraints for any set of evaluation measures from this class. A number of properties of the representation is derived as well.
Author Marzena Kryszkiewicz (FEIT / IN)
Marzena Kryszkiewicz,,
- The Institute of Computer Science
Publication size in sheets0.55
Book Nguyen Ngoc Thanh, Trawiński Bogdan, Fujita Hamido, Hong Tzung-Pei (eds.): Intelligent Information and Database Systems. Proceedings of the 8th Asian Conference, ACIIDS 2016, Part II, Lecture Notes In Computer Science, vol. 9622, 2016, Berlin Heidelberg, Springer Berlin Heidelberg, ISBN 978-3-662-49389-2, [978-3-662-49390-8], 811 p., DOI:10.1007/978-3-662-49390-8
front matter.pdf / 299.03 KB / No licence information
URL http://link.springer.com/chapter/10.1007/978-3-662-49390-8_14
ProjectDevelopment of new algorithms in the areas of software and computer architecture, artificial intelligence and information systems and computer graphics . Project leader: Rybiński Henryk, , Phone: +48 22 234 7731, start date 18-05-2015, end date 30-11-2016, II/2015/DS/1, Completed
WEiTI Działalność statutowa
Languageen angielski
Score (nominal)15
Score sourceconferenceIndex
ScoreMinisterial score = 15.0, 01-02-2020, BookChapterSeriesAndMatConfByConferenceseries
Ministerial score (2013-2016) = 15.0, 01-02-2020, BookChapterSeriesAndMatConfByConferenceseries
Publication indicators WoS Citations = 3; Scopus Citations = 3; GS Citations = 3.0
Citation count*3 (2020-09-06)
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