Application of data mining techniques in resources evaluation

Hai Dong Meng; Yu Chen Song; Dinil Pushpalal

doi:10.1109/IITA-GRS.2010.5602611

Application of data mining techniques in resources evaluation

Hai Dong Meng, Yu Chen Song, Dinil Pushpalal

CUL - International Cultural Studies

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

1 Citation (Scopus)

Abstract

Due to the complexity of geoscientific data, such as geochemical data, geophysical data and digital remote sensing data, traditional data mining methods, such as cluster analysis and association analysis, have limitations in resources evaluation. In this paper, a clustering algorithm is presented which has the ability to handle clusters of arbitrary shapes, sizes and densities. For association analysis, quantitative association rules aims to deal with the relationships among continuous attributes of geoscientific data objects in resources evaluation. An association analysis algorithm based on the distances between clusters projected on attributes is presented. Applications indicate that the algorithms are effective in real world applications.

Original language	English
Title of host publication	2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010
Pages	510-513
Number of pages	4
DOIs	https://doi.org/10.1109/IITA-GRS.2010.5602611
Publication status	Published - 2010 Nov 22
Event	2010 2nd IITA Conference on Geoscience and Remote Sensing, IITA-GRS 2010 - Qingdao, China Duration: 2010 Aug 28 → 2010 Aug 31

Publication series

Name	2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010
Volume	1

Other

Other	2010 2nd IITA Conference on Geoscience and Remote Sensing, IITA-GRS 2010
Country/Territory	China
City	Qingdao
Period	10/8/28 → 10/8/31

Keywords

Association analysis
Cluster analysis
Data mining
Geoscientific data
Spatial data mining

ASJC Scopus subject areas

Geophysics
Geotechnical Engineering and Engineering Geology
Computer Networks and Communications

Access to Document

10.1109/IITA-GRS.2010.5602611

Cite this

Meng, H. D., Song, Y. C., & Pushpalal, D. (2010). Application of data mining techniques in resources evaluation. In 2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010 (pp. 510-513). [5602611] (2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010; Vol. 1). https://doi.org/10.1109/IITA-GRS.2010.5602611

Application of data mining techniques in resources evaluation. / Meng, Hai Dong; Song, Yu Chen; Pushpalal, Dinil.

2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010. 2010. p. 510-513 5602611 (2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010; Vol. 1).

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

Meng, HD, Song, YC & Pushpalal, D 2010, Application of data mining techniques in resources evaluation. in 2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010., 5602611, 2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010, vol. 1, pp. 510-513, 2010 2nd IITA Conference on Geoscience and Remote Sensing, IITA-GRS 2010, Qingdao, China, 10/8/28. https://doi.org/10.1109/IITA-GRS.2010.5602611

@inproceedings{143d65d8e5e1434ba3b980f38dce4189,

title = "Application of data mining techniques in resources evaluation",

abstract = "Due to the complexity of geoscientific data, such as geochemical data, geophysical data and digital remote sensing data, traditional data mining methods, such as cluster analysis and association analysis, have limitations in resources evaluation. In this paper, a clustering algorithm is presented which has the ability to handle clusters of arbitrary shapes, sizes and densities. For association analysis, quantitative association rules aims to deal with the relationships among continuous attributes of geoscientific data objects in resources evaluation. An association analysis algorithm based on the distances between clusters projected on attributes is presented. Applications indicate that the algorithms are effective in real world applications.",

keywords = "Association analysis, Cluster analysis, Data mining, Geoscientific data, Spatial data mining",

author = "Meng, {Hai Dong} and Song, {Yu Chen} and Dinil Pushpalal",

year = "2010",

month = nov,

day = "22",

doi = "10.1109/IITA-GRS.2010.5602611",

language = "English",

isbn = "9781424485154",

series = "2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010",

pages = "510--513",

booktitle = "2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010",

note = "2010 2nd IITA Conference on Geoscience and Remote Sensing, IITA-GRS 2010 ; Conference date: 28-08-2010 Through 31-08-2010",

}

TY - GEN

T1 - Application of data mining techniques in resources evaluation

AU - Meng, Hai Dong

AU - Song, Yu Chen

AU - Pushpalal, Dinil

PY - 2010/11/22

Y1 - 2010/11/22

N2 - Due to the complexity of geoscientific data, such as geochemical data, geophysical data and digital remote sensing data, traditional data mining methods, such as cluster analysis and association analysis, have limitations in resources evaluation. In this paper, a clustering algorithm is presented which has the ability to handle clusters of arbitrary shapes, sizes and densities. For association analysis, quantitative association rules aims to deal with the relationships among continuous attributes of geoscientific data objects in resources evaluation. An association analysis algorithm based on the distances between clusters projected on attributes is presented. Applications indicate that the algorithms are effective in real world applications.

AB - Due to the complexity of geoscientific data, such as geochemical data, geophysical data and digital remote sensing data, traditional data mining methods, such as cluster analysis and association analysis, have limitations in resources evaluation. In this paper, a clustering algorithm is presented which has the ability to handle clusters of arbitrary shapes, sizes and densities. For association analysis, quantitative association rules aims to deal with the relationships among continuous attributes of geoscientific data objects in resources evaluation. An association analysis algorithm based on the distances between clusters projected on attributes is presented. Applications indicate that the algorithms are effective in real world applications.

KW - Association analysis

KW - Cluster analysis

KW - Data mining

KW - Geoscientific data

KW - Spatial data mining

UR - http://www.scopus.com/inward/record.url?scp=78349256741&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78349256741&partnerID=8YFLogxK

U2 - 10.1109/IITA-GRS.2010.5602611

DO - 10.1109/IITA-GRS.2010.5602611

M3 - Conference contribution

AN - SCOPUS:78349256741

SN - 9781424485154

T3 - 2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010

SP - 510

EP - 513

BT - 2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010

T2 - 2010 2nd IITA Conference on Geoscience and Remote Sensing, IITA-GRS 2010

Y2 - 28 August 2010 through 31 August 2010

ER -

Application of data mining techniques in resources evaluation

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this