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Towards Finding Maximal Subrelations with Desired Properties

  • As soon as data is noisy, knowledge as it is represented in an information system becomes unreliable. Features in databases induce equivalence relations—but knowledge discovery takes the other way round: given a relation, what could be a suitable functional description? But the relations we work on are noisy again. If we expect to record data for learning a classification of objects then it can well be the real data does not create a reflexive, symmetric and transitive relation although we know it should be. The usual approach taken here is to build the closure in order to ensure desired properties. This, however, leads to overgeneralisation rather quickly.

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Document Type:Conference Object
Author:Martin Eric Müller
Parent Title (English):Höfner, Jipsen et al. (Eds.): Relational and Algebraic Methods in Computer Science. 14th International Conference, RAMiCS 2014, Marienstatt, Germany, April 28-May 1, 2014, Proceedings. Lecture Notes in Computer Science (LNCS), Vol. 8428
First Page:344
Last Page:361
Publication year:2014
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2017/01/13