Iterative local gaussian clustering to extract interesting patterns on spatio-temporal database

The study of spatio-temporal data mining in extracting and analyzing interesting patterns from spatio-temporal database has attract great interest in diverse research field. Huge amount of research has been done in either spatial data mining or temporal data mining and numbers of clustering algorith...

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Main Author:	Aman, Tirwani
Format:	Thesis
Language:	English
Published:	2009
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/18367/1/TirwaniBintiAmanMFC2009_TheStudyOfSpatio-TemporalDataMining.pdf
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id	my-utm-ep.18367
record_format	uketd_dc
spelling	my-utm-ep.183672017-06-14T01:08:12Z Iterative local gaussian clustering to extract interesting patterns on spatio-temporal database 2009 Aman, Tirwani QA75 Electronic computers. Computer science The study of spatio-temporal data mining in extracting and analyzing interesting patterns from spatio-temporal database has attract great interest in diverse research field. Huge amount of research has been done in either spatial data mining or temporal data mining and numbers of clustering algorithms have been proposed. However, not much research has been done in the integration of both spatial and temporal data mining, which is spatio-temporal data mining. The focuses of this study is to analyses the Iterative Local Gaussian Clustering (ILGC) algorithm and implement the algorithm to the spatio-temporal data, which is crime data. . In ILGC approach, the K- nearest neighbor (KNN) density estimation is extended and combined with Gaussian kernel function, where KNN contribute in determining the best local data iteratively for Gaussian kernel density estimation. The local best is defined as the set of neighbors data that maximizes the Gaussian kernel function. ILGC used Bayesian rule in dealing with the problem of selecting best local data. To test and validate the ILGC approach, other clustering method, which is K-Means and Self Organizing Map (SOM) will be implemented on the same data sets. 2009 Thesis http://eprints.utm.my/id/eprint/18367/ http://eprints.utm.my/id/eprint/18367/1/TirwaniBintiAmanMFC2009_TheStudyOfSpatio-TemporalDataMining.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:72694?site_name=Restricted Repository masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Aman, Tirwani Iterative local gaussian clustering to extract interesting patterns on spatio-temporal database
description	The study of spatio-temporal data mining in extracting and analyzing interesting patterns from spatio-temporal database has attract great interest in diverse research field. Huge amount of research has been done in either spatial data mining or temporal data mining and numbers of clustering algorithms have been proposed. However, not much research has been done in the integration of both spatial and temporal data mining, which is spatio-temporal data mining. The focuses of this study is to analyses the Iterative Local Gaussian Clustering (ILGC) algorithm and implement the algorithm to the spatio-temporal data, which is crime data. . In ILGC approach, the K- nearest neighbor (KNN) density estimation is extended and combined with Gaussian kernel function, where KNN contribute in determining the best local data iteratively for Gaussian kernel density estimation. The local best is defined as the set of neighbors data that maximizes the Gaussian kernel function. ILGC used Bayesian rule in dealing with the problem of selecting best local data. To test and validate the ILGC approach, other clustering method, which is K-Means and Self Organizing Map (SOM) will be implemented on the same data sets.
format	Thesis
qualification_level	Master's degree
author	Aman, Tirwani
author_facet	Aman, Tirwani
author_sort	Aman, Tirwani
title	Iterative local gaussian clustering to extract interesting patterns on spatio-temporal database
title_short	Iterative local gaussian clustering to extract interesting patterns on spatio-temporal database
title_full	Iterative local gaussian clustering to extract interesting patterns on spatio-temporal database
title_fullStr	Iterative local gaussian clustering to extract interesting patterns on spatio-temporal database
title_full_unstemmed	Iterative local gaussian clustering to extract interesting patterns on spatio-temporal database
title_sort	iterative local gaussian clustering to extract interesting patterns on spatio-temporal database
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2009
url	http://eprints.utm.my/id/eprint/18367/1/TirwaniBintiAmanMFC2009_TheStudyOfSpatio-TemporalDataMining.pdf
_version_	1747815257626116096

Iterative local gaussian clustering to extract interesting patterns on spatio-temporal database

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