An approach for mining complex spatial dataset

Grace Samson, Joan Lu, Lizen Wang, Dave Wilson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Abstract: Spatial data mining organizes by location what
is interesting as such, specific features of spatial data
mining (including observations that are not independent
and spatial autocorrelation among the features) that
preclude the use of general purpose data mining
algorithms poses a serious challenge in the task of
mining meaningful patterns from spatial systems. This
creates the complexity that characterises complex spatial
systems. Thus, the major challenge for a spatial data
miner in trying to build a general complex spatial model
would be; to be able to integrate the elements of these
complex systems in a way that is optimally effective in
any particular case. We have examined ways of creating
explicit spatial model that represents an application of
mining techniques capable of analysing data from a
complex spatial system and then producing information
that would be useful in various disciplines where spatial
data form the basis of general interest
Original languageEnglish
Title of host publicationProceedings of the International Conference on Information and Knowledge Engineering
EditorsHamid R. Arabnia, Leonidas Deligiannidis, Ray Hashemi, Joan Lu
PublisherCSREA Press
Pages129-135
Number of pages7
ISBN (Print)1601322518
Publication statusPublished - 2013
EventInternational Conference on Information and Knowledge Engineering - Las Vegas, United States
Duration: 22 Jul 201325 Jul 2013
http://worldcomp-proceedings.com/proc/proc2013/ike.html

Conference

ConferenceInternational Conference on Information and Knowledge Engineering
Abbreviated titleIKE'13
CountryUnited States
CityLas Vegas
Period22/07/1325/07/13
Internet address

Fingerprint

data mining
spatial data
autocorrelation

Cite this

Samson, G., Lu, J., Wang, L., & Wilson, D. (2013). An approach for mining complex spatial dataset. In H. R. Arabnia, L. Deligiannidis, R. Hashemi, & J. Lu (Eds.), Proceedings of the International Conference on Information and Knowledge Engineering (pp. 129-135). CSREA Press.
Samson, Grace ; Lu, Joan ; Wang, Lizen ; Wilson, Dave. / An approach for mining complex spatial dataset. Proceedings of the International Conference on Information and Knowledge Engineering. editor / Hamid R. Arabnia ; Leonidas Deligiannidis ; Ray Hashemi ; Joan Lu. CSREA Press, 2013. pp. 129-135
@inproceedings{3d85bb8414d74f05bf5762bd5fb6ae7e,
title = "An approach for mining complex spatial dataset",
abstract = "Abstract: Spatial data mining organizes by location whatis interesting as such, specific features of spatial datamining (including observations that are not independentand spatial autocorrelation among the features) thatpreclude the use of general purpose data miningalgorithms poses a serious challenge in the task ofmining meaningful patterns from spatial systems. Thiscreates the complexity that characterises complex spatialsystems. Thus, the major challenge for a spatial dataminer in trying to build a general complex spatial modelwould be; to be able to integrate the elements of thesecomplex systems in a way that is optimally effective inany particular case. We have examined ways of creatingexplicit spatial model that represents an application ofmining techniques capable of analysing data from acomplex spatial system and then producing informationthat would be useful in various disciplines where spatialdata form the basis of general interest",
keywords = "Spatial data, Complex systems, Patterns mining, Spatial models, Spatial database",
author = "Grace Samson and Joan Lu and Lizen Wang and Dave Wilson",
year = "2013",
language = "English",
isbn = "1601322518",
pages = "129--135",
editor = "Arabnia, {Hamid R.} and Leonidas Deligiannidis and Ray Hashemi and Joan Lu",
booktitle = "Proceedings of the International Conference on Information and Knowledge Engineering",
publisher = "CSREA Press",

}

Samson, G, Lu, J, Wang, L & Wilson, D 2013, An approach for mining complex spatial dataset. in HR Arabnia, L Deligiannidis, R Hashemi & J Lu (eds), Proceedings of the International Conference on Information and Knowledge Engineering. CSREA Press, pp. 129-135, International Conference on Information and Knowledge Engineering, Las Vegas, United States, 22/07/13.

An approach for mining complex spatial dataset. / Samson, Grace; Lu, Joan; Wang, Lizen ; Wilson, Dave.

Proceedings of the International Conference on Information and Knowledge Engineering. ed. / Hamid R. Arabnia; Leonidas Deligiannidis; Ray Hashemi; Joan Lu. CSREA Press, 2013. p. 129-135.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - An approach for mining complex spatial dataset

AU - Samson, Grace

AU - Lu, Joan

AU - Wang, Lizen

AU - Wilson, Dave

PY - 2013

Y1 - 2013

N2 - Abstract: Spatial data mining organizes by location whatis interesting as such, specific features of spatial datamining (including observations that are not independentand spatial autocorrelation among the features) thatpreclude the use of general purpose data miningalgorithms poses a serious challenge in the task ofmining meaningful patterns from spatial systems. Thiscreates the complexity that characterises complex spatialsystems. Thus, the major challenge for a spatial dataminer in trying to build a general complex spatial modelwould be; to be able to integrate the elements of thesecomplex systems in a way that is optimally effective inany particular case. We have examined ways of creatingexplicit spatial model that represents an application ofmining techniques capable of analysing data from acomplex spatial system and then producing informationthat would be useful in various disciplines where spatialdata form the basis of general interest

AB - Abstract: Spatial data mining organizes by location whatis interesting as such, specific features of spatial datamining (including observations that are not independentand spatial autocorrelation among the features) thatpreclude the use of general purpose data miningalgorithms poses a serious challenge in the task ofmining meaningful patterns from spatial systems. Thiscreates the complexity that characterises complex spatialsystems. Thus, the major challenge for a spatial dataminer in trying to build a general complex spatial modelwould be; to be able to integrate the elements of thesecomplex systems in a way that is optimally effective inany particular case. We have examined ways of creatingexplicit spatial model that represents an application ofmining techniques capable of analysing data from acomplex spatial system and then producing informationthat would be useful in various disciplines where spatialdata form the basis of general interest

KW - Spatial data

KW - Complex systems

KW - Patterns mining

KW - Spatial models

KW - Spatial database

UR - http://worldcomp-proceedings.com/proc/proc2013/ike.html

M3 - Conference contribution

SN - 1601322518

SP - 129

EP - 135

BT - Proceedings of the International Conference on Information and Knowledge Engineering

A2 - Arabnia, Hamid R.

A2 - Deligiannidis, Leonidas

A2 - Hashemi, Ray

A2 - Lu, Joan

PB - CSREA Press

ER -

Samson G, Lu J, Wang L, Wilson D. An approach for mining complex spatial dataset. In Arabnia HR, Deligiannidis L, Hashemi R, Lu J, editors, Proceedings of the International Conference on Information and Knowledge Engineering. CSREA Press. 2013. p. 129-135