Implications of probabilistic data modeling for mining association rules

Michael Hahsler; Kurt Hornik; Thomas Reutterer

doi:10.1007/3-540-31314-1_73

Implications of probabilistic data modeling for mining association rules

Michael Hahsler, Kurt Hornik, Thomas Reutterer

Institute for Data, Process and Knowledge Management (AE Sabou)

Publication: Chapter in book/Conference proceeding › Contribution to conference proceedings

Original language	English
Title of host publication	From Data and Information Analysis to Knowledge Engineering
Subtitle of host publication	Studies in Classification, Data Analysis, and Knowledge Organization
Editors	M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger, and W. Gaul
Place of Publication	Berlin
Publisher	Springer
Pages	598 - 605
ISBN (Electronic)	978-3-540-31314-4
ISBN (Print)	3-540-31313-3
DOIs	https://doi.org/10.1007/3-540-31314-1_73
Publication status	Published - 1 Jul 2006

Austrian Classification of Fields of Science and Technology (ÖFOS)

502019 Marketing
102

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10.1007/3-540-31314-1_73

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University Library Catalogue

A computational environment for mining association rules and frequent item sets in R
Hahsler, M. (PI - Project head) & Hornik, K. (PI - Project head)
6/01/04 → 5/01/08
Project: Research funding

Cite this

Hahsler, M., Hornik, K., & Reutterer, T. (2006). Implications of probabilistic data modeling for mining association rules. In M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger, and W. Gaul (Ed.), From Data and Information Analysis to Knowledge Engineering : Studies in Classification, Data Analysis, and Knowledge Organization (pp. 598 - 605). Springer. https://doi.org/10.1007/3-540-31314-1_73

Hahsler, Michael ; Hornik, Kurt ; Reutterer, Thomas. / Implications of probabilistic data modeling for mining association rules. From Data and Information Analysis to Knowledge Engineering : Studies in Classification, Data Analysis, and Knowledge Organization. editor / M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger, and W. Gaul. Berlin : Springer, 2006. pp. 598 - 605

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title = "Implications of probabilistic data modeling for mining association rules",

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Hahsler, M, Hornik, K & Reutterer, T 2006, Implications of probabilistic data modeling for mining association rules. in M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger, and W. Gaul (ed.), From Data and Information Analysis to Knowledge Engineering : Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, pp. 598 - 605. https://doi.org/10.1007/3-540-31314-1_73

Implications of probabilistic data modeling for mining association rules. / Hahsler, Michael; Hornik, Kurt ; Reutterer, Thomas.
From Data and Information Analysis to Knowledge Engineering : Studies in Classification, Data Analysis, and Knowledge Organization. ed. / M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger, and W. Gaul. Berlin: Springer, 2006. p. 598 - 605.

Publication: Chapter in book/Conference proceeding › Contribution to conference proceedings

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T1 - Implications of probabilistic data modeling for mining association rules

AU - Hahsler, Michael

AU - Hornik, Kurt

AU - Reutterer, Thomas

PY - 2006/7/1

Y1 - 2006/7/1

U2 - 10.1007/3-540-31314-1_73

DO - 10.1007/3-540-31314-1_73

M3 - Contribution to conference proceedings

SN - 3-540-31313-3

SP - 598

EP - 605

BT - From Data and Information Analysis to Knowledge Engineering

A2 - M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger, and W. Gaul, null

PB - Springer

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Hahsler M, Hornik K , Reutterer T. Implications of probabilistic data modeling for mining association rules. In M. Spiliopoulou, R. Kruse, C. Borgelt, A. Nürnberger, and W. Gaul, editor, From Data and Information Analysis to Knowledge Engineering : Studies in Classification, Data Analysis, and Knowledge Organization. Berlin: Springer. 2006. p. 598 - 605 doi: 10.1007/3-540-31314-1_73

Implications of probabilistic data modeling for mining association rules

Austrian Classification of Fields of Science and Technology (ÖFOS)

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Projects

A computational environment for mining association rules and frequent item sets in R

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