Nathan C. Hiers

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dblp:06/2100 · DBLP profile ↗

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1ranked-venue papers

0as first author

0since 2021 · last 2008

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval

1 paper

Data mining · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Data mining probabilistic graphical models	0.1	1	2008	Spatiotemporal Relational Probability Trees: An Introduction · ICDM 2008
Data mining › predictive modeling › classification › decision tree learning probability estimation trees	0.1	1	2008	Spatiotemporal Relational Probability Trees: An Introduction · ICDM 2008
Data mining › structured data mining relational data mining	0.1	1	2008	Spatiotemporal Relational Probability Trees: An Introduction · ICDM 2008
Data mining spatiotemporal data mining	0.1	1	2008	Spatiotemporal Relational Probability Trees: An Introduction · ICDM 2008

Methods — techniques the papers use, named apart from their topics

sampling · 0.1probability estimation trees · 0.1

Year	Publication	Venue	Position
2008	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. Amy McGovern, Nathan C. Hiers, Matthew W. Collier, David John Gagne II, Rodger A. Brown	ICDM	2