Spatiotemporal relational probability trees: An introduction

Amy McGovern; Nathan C. Hiers; Matthew Collier; David J. Gagne; Rodger A. Brown

doi:10.1109/ICDM.2008.134

Spatiotemporal relational probability trees: An introduction

Amy McGovern, Nathan C. Hiers, Matthew Collier, David J. Gagne, Rodger A. Brown

Machine Integration and Learning for Earth Systems

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

27 Scopus citations

Abstract

We introduce spatiotemporal relational probability trees (SRPTs), probability estimation trees for relational data that can vary in both space and time. The SRPT algorithm addresses the exponential increase in search complexity through sampling. We validate the SRPT using a simulated data set and we empirically demonstrate the SRPT algorithm on two real-world data sets.

Original language	English
Title of host publication	Proceedings - 8th IEEE International Conference on Data Mining, ICDM 2008
Pages	935-940
Number of pages	6
DOIs	https://doi.org/10.1109/ICDM.2008.134
State	Published - 2008
Event	8th IEEE International Conference on Data Mining, ICDM 2008 - Pisa, Italy Duration: Dec 15 2008 → Dec 19 2008

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)	1550-4786

Conference

Conference	8th IEEE International Conference on Data Mining, ICDM 2008
Country/Territory	Italy
City	Pisa
Period	12/15/08 → 12/19/08

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10.1109/ICDM.2008.134

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@inproceedings{1b723c8f57234ce69ae9243b8cd01823,

title = "Spatiotemporal relational probability trees: An introduction",

abstract = "We introduce spatiotemporal relational probability trees (SRPTs), probability estimation trees for relational data that can vary in both space and time. The SRPT algorithm addresses the exponential increase in search complexity through sampling. We validate the SRPT using a simulated data set and we empirically demonstrate the SRPT algorithm on two real-world data sets.",

author = "Amy McGovern and Hiers, \{Nathan C.\} and Matthew Collier and Gagne, \{David J.\} and Brown, \{Rodger A.\}",

year = "2008",

doi = "10.1109/ICDM.2008.134",

language = "English",

isbn = "9780769535029",

series = "Proceedings - IEEE International Conference on Data Mining, ICDM",

pages = "935--940",

booktitle = "Proceedings - 8th IEEE International Conference on Data Mining, ICDM 2008",

note = "8th IEEE International Conference on Data Mining, ICDM 2008 ; Conference date: 15-12-2008 Through 19-12-2008",

}

McGovern, A, Hiers, NC, Collier, M, Gagne, DJ & Brown, RA 2008, Spatiotemporal relational probability trees: An introduction. in Proceedings - 8th IEEE International Conference on Data Mining, ICDM 2008., 4781204, Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 935-940, 8th IEEE International Conference on Data Mining, ICDM 2008, Pisa, Italy, 12/15/08. https://doi.org/10.1109/ICDM.2008.134

Spatiotemporal relational probability trees: An introduction. / McGovern, Amy; Hiers, Nathan C.; Collier, Matthew et al.
Proceedings - 8th IEEE International Conference on Data Mining, ICDM 2008. 2008. p. 935-940 4781204 (Proceedings - IEEE International Conference on Data Mining, ICDM).

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

TY - GEN

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AU - Gagne, David J.

AU - Brown, Rodger A.

PY - 2008

Y1 - 2008

N2 - We introduce spatiotemporal relational probability trees (SRPTs), probability estimation trees for relational data that can vary in both space and time. The SRPT algorithm addresses the exponential increase in search complexity through sampling. We validate the SRPT using a simulated data set and we empirically demonstrate the SRPT algorithm on two real-world data sets.

AB - We introduce spatiotemporal relational probability trees (SRPTs), probability estimation trees for relational data that can vary in both space and time. The SRPT algorithm addresses the exponential increase in search complexity through sampling. We validate the SRPT using a simulated data set and we empirically demonstrate the SRPT algorithm on two real-world data sets.

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DO - 10.1109/ICDM.2008.134

M3 - Conference contribution

AN - SCOPUS:67049143498

SN - 9780769535029

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BT - Proceedings - 8th IEEE International Conference on Data Mining, ICDM 2008

Y2 - 15 December 2008 through 19 December 2008

ER -

Spatiotemporal relational probability trees: An introduction

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