Mining determining sets for partially defined functions

Dan A. Simovici; Dan Pletea; Rosanne Vetro

doi:10.1007/978-3-642-03067-3_28

Mining determining sets for partially defined functions

Dan A. Simovici
, Dan Pletea
, Rosanne Vetro

Computer Science

University of Massachusetts Boston

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper describes an algorithm that determines the minimal sets of variables that determine the values of a discrete partial function. The Apriori-like algorithm is based on the dual hereditary property of determining sets. Experimental results are provided that demonstrate the efficiency of the algorithm for functions with up to 24 variables. The dependency of the number of minimal determining sets on the size of the specification of the partial function is also examined.

Original language	English
Title of host publication	Advances in Data Mining
Subtitle of host publication	Applications and Theoretical Aspects - 9th Industrial Conference, ICDM 2009, Proceedings
Pages	353-360
Number of pages	8
DOIs	https://doi.org/10.1007/978-3-642-03067-3_28
State	Published - 2009
Event	9th Industrial Conference on Advances in Data Mining: Applications and Theoretical Aspects, ICDM 2009 - Leipzig, Germany Duration: Jul 20 2009 → Jul 22 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5633 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th Industrial Conference on Advances in Data Mining: Applications and Theoretical Aspects, ICDM 2009
Country/Territory	Germany
City	Leipzig
Period	7/20/09 → 7/22/09

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-03067-3_28

Cite this

Simovici, D. A., Pletea, D., & Vetro, R. (2009). Mining determining sets for partially defined functions. In Advances in Data Mining: Applications and Theoretical Aspects - 9th Industrial Conference, ICDM 2009, Proceedings (pp. 353-360). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5633 LNAI). https://doi.org/10.1007/978-3-642-03067-3_28

Mining determining sets for partially defined functions. / Simovici, Dan A.; Pletea, Dan; Vetro, Rosanne.
Advances in Data Mining: Applications and Theoretical Aspects - 9th Industrial Conference, ICDM 2009, Proceedings. 2009. p. 353-360 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5633 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Simovici, DA, Pletea, D & Vetro, R 2009, Mining determining sets for partially defined functions. in Advances in Data Mining: Applications and Theoretical Aspects - 9th Industrial Conference, ICDM 2009, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5633 LNAI, pp. 353-360, 9th Industrial Conference on Advances in Data Mining: Applications and Theoretical Aspects, ICDM 2009, Leipzig, Germany, 7/20/09. https://doi.org/10.1007/978-3-642-03067-3_28

Simovici DA, Pletea D, Vetro R. Mining determining sets for partially defined functions. In Advances in Data Mining: Applications and Theoretical Aspects - 9th Industrial Conference, ICDM 2009, Proceedings. 2009. p. 353-360. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-03067-3_28

Simovici, Dan A. ; Pletea, Dan ; Vetro, Rosanne. / Mining determining sets for partially defined functions. Advances in Data Mining: Applications and Theoretical Aspects - 9th Industrial Conference, ICDM 2009, Proceedings. 2009. pp. 353-360 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Mining determining sets for partially defined functions

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