A spatial-based KDD process to better understand the spatiotemporal phenomena

Hugo Alatrista-Salas

A spatial-based KDD process to better understand the spatiotemporal phenomena

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Research output: Contribution to journal › Conference article › peer-review

Abstract

In this paper, we present a knowledge discovery process applied to hydrological data. To achieve this objective, we combine successive methods to extract knowledge on data collected at stations located along several rivers. Firstly, data is pre processed in order to obtain diffierent spatial proximities. Later, we apply two algorithms to extract spatiotemporal patterns and compare them. Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and rivers monitoring pressure data.

Original language	English
Journal	CEUR Workshop Proceedings
Volume	1001
State	Published - 2013
Externally published	Yes
Event	2013 Doctoral Consortium of the 25th International Conference on Advanced Information Systems Engineering, CAiSE 2013 - Valencia, Spain Duration: 21 Jun 2013 → 21 Jun 2013

Keywords

Data mining
Sequential patterns
Spatiotemporal data

Cite this

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title = "A spatial-based KDD process to better understand the spatiotemporal phenomena",

abstract = "In this paper, we present a knowledge discovery process applied to hydrological data. To achieve this objective, we combine successive methods to extract knowledge on data collected at stations located along several rivers. Firstly, data is pre processed in order to obtain diffierent spatial proximities. Later, we apply two algorithms to extract spatiotemporal patterns and compare them. Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and rivers monitoring pressure data.",

keywords = "Data mining, Sequential patterns, Spatiotemporal data",

author = "Hugo Alatrista-Salas",

year = "2013",

language = "English",

volume = "1001",

journal = "CEUR Workshop Proceedings",

issn = "1613-0073",

note = "2013 Doctoral Consortium of the 25th International Conference on Advanced Information Systems Engineering, CAiSE 2013 ; Conference date: 21-06-2013 Through 21-06-2013",

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T1 - A spatial-based KDD process to better understand the spatiotemporal phenomena

AU - Alatrista-Salas, Hugo

PY - 2013

Y1 - 2013

N2 - In this paper, we present a knowledge discovery process applied to hydrological data. To achieve this objective, we combine successive methods to extract knowledge on data collected at stations located along several rivers. Firstly, data is pre processed in order to obtain diffierent spatial proximities. Later, we apply two algorithms to extract spatiotemporal patterns and compare them. Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and rivers monitoring pressure data.

AB - In this paper, we present a knowledge discovery process applied to hydrological data. To achieve this objective, we combine successive methods to extract knowledge on data collected at stations located along several rivers. Firstly, data is pre processed in order to obtain diffierent spatial proximities. Later, we apply two algorithms to extract spatiotemporal patterns and compare them. Such elements can be used to assess spatialized indicators to assist the interpretation of ecological and rivers monitoring pressure data.

KW - Data mining

KW - Sequential patterns

KW - Spatiotemporal data

UR - http://www.scopus.com/inward/record.url?scp=84961304134&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:84961304134

SN - 1613-0073

VL - 1001

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

T2 - 2013 Doctoral Consortium of the 25th International Conference on Advanced Information Systems Engineering, CAiSE 2013

Y2 - 21 June 2013 through 21 June 2013

ER -

A spatial-based KDD process to better understand the spatiotemporal phenomena

Abstract

Keywords

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