Interpolation models for spatiotemporal association mining

Research output: Contribution to journalArticle

Abstract

In this paper, we investigate interpolation methods that are suitable for discovering spatiotemporal association rules for unsampled sites with a focus on drought risk management problem. For drought risk management, raw weather data is collected, converted to various indices, and then mined for association rules. To generate association rules for the unsampled sites, interpolation methods can be applied at any stage of this data mining process. We develop and integrate three interpolation models into our association rule mining algorithm. We call them pre-order, in-order and post-order interpolation models. The performance of these three models is experimentally evaluated comparing the interpolated association rules with the rules discovered from actual raw data based on two metrics, precision and recall. Our experiments show that the post-order interpolation model provides the highest precision among the three models, and the Kriging method in the pre-order interpolation model presents the highest recall.

Original languageEnglish (US)
Pages (from-to)153-172
Number of pages20
JournalFundamenta Informaticae
Volume59
Issue number2-3
StatePublished - Feb 1 2004

Fingerprint

Association rules
Mining
Interpolation
Interpolate
Association Rules
Drought
Preorder
Interpolation Method
Risk Management
Risk management
Model
Association Rule Mining
Kriging
Weather
Data mining
Data Mining
Integrate
Metric
Experiment
Experiments

Keywords

  • Association rules
  • Cross-validation
  • Data Mmning
  • Geo-spatial decision support system
  • Interpolation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Algebra and Number Theory
  • Information Systems
  • Computational Theory and Mathematics

Cite this

Interpolation models for spatiotemporal association mining. / Li, Dan; Deogun, Jitender S.

In: Fundamenta Informaticae, Vol. 59, No. 2-3, 01.02.2004, p. 153-172.

Research output: Contribution to journalArticle

@article{e2c9c93900f14759b63b89c62964a8ea,
title = "Interpolation models for spatiotemporal association mining",
abstract = "In this paper, we investigate interpolation methods that are suitable for discovering spatiotemporal association rules for unsampled sites with a focus on drought risk management problem. For drought risk management, raw weather data is collected, converted to various indices, and then mined for association rules. To generate association rules for the unsampled sites, interpolation methods can be applied at any stage of this data mining process. We develop and integrate three interpolation models into our association rule mining algorithm. We call them pre-order, in-order and post-order interpolation models. The performance of these three models is experimentally evaluated comparing the interpolated association rules with the rules discovered from actual raw data based on two metrics, precision and recall. Our experiments show that the post-order interpolation model provides the highest precision among the three models, and the Kriging method in the pre-order interpolation model presents the highest recall.",
keywords = "Association rules, Cross-validation, Data Mmning, Geo-spatial decision support system, Interpolation",
author = "Dan Li and Deogun, {Jitender S.}",
year = "2004",
month = "2",
day = "1",
language = "English (US)",
volume = "59",
pages = "153--172",
journal = "Fundamenta Informaticae",
issn = "0169-2968",
publisher = "IOS Press",
number = "2-3",

}

TY - JOUR

T1 - Interpolation models for spatiotemporal association mining

AU - Li, Dan

AU - Deogun, Jitender S.

PY - 2004/2/1

Y1 - 2004/2/1

N2 - In this paper, we investigate interpolation methods that are suitable for discovering spatiotemporal association rules for unsampled sites with a focus on drought risk management problem. For drought risk management, raw weather data is collected, converted to various indices, and then mined for association rules. To generate association rules for the unsampled sites, interpolation methods can be applied at any stage of this data mining process. We develop and integrate three interpolation models into our association rule mining algorithm. We call them pre-order, in-order and post-order interpolation models. The performance of these three models is experimentally evaluated comparing the interpolated association rules with the rules discovered from actual raw data based on two metrics, precision and recall. Our experiments show that the post-order interpolation model provides the highest precision among the three models, and the Kriging method in the pre-order interpolation model presents the highest recall.

AB - In this paper, we investigate interpolation methods that are suitable for discovering spatiotemporal association rules for unsampled sites with a focus on drought risk management problem. For drought risk management, raw weather data is collected, converted to various indices, and then mined for association rules. To generate association rules for the unsampled sites, interpolation methods can be applied at any stage of this data mining process. We develop and integrate three interpolation models into our association rule mining algorithm. We call them pre-order, in-order and post-order interpolation models. The performance of these three models is experimentally evaluated comparing the interpolated association rules with the rules discovered from actual raw data based on two metrics, precision and recall. Our experiments show that the post-order interpolation model provides the highest precision among the three models, and the Kriging method in the pre-order interpolation model presents the highest recall.

KW - Association rules

KW - Cross-validation

KW - Data Mmning

KW - Geo-spatial decision support system

KW - Interpolation

UR - http://www.scopus.com/inward/record.url?scp=2342665727&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=2342665727&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:2342665727

VL - 59

SP - 153

EP - 172

JO - Fundamenta Informaticae

JF - Fundamenta Informaticae

SN - 0169-2968

IS - 2-3

ER -