Improving the tool for analyzing Malaysia’s demographic change: data standardization analysis to form geo-demographics classification profiles using k-means algorithms
Clustering is one of the important methods in data exploratory in this era because it is widely applied in data mining.Clustering of data is necessary to produce geo-demographic classification where k-means algorithm is used as cluster algorithm. K-means is one of the methods commonly used in clus...
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| Language: | English |
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School of Social, Development and Environmental Studies, Faculty of Social Science and Humanities, Universiti Kebangsaan Malaysia
2016
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| Online Access: | http://journalarticle.ukm.my/10309/ http://journalarticle.ukm.my/10309/ http://journalarticle.ukm.my/10309/1/4x.geografia-siupsi-mei16-Kamarul-edam.pdf |
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ukm-103092017-04-18T08:21:27Z http://journalarticle.ukm.my/10309/ Improving the tool for analyzing Malaysia’s demographic change: data standardization analysis to form geo-demographics classification profiles using k-means algorithms Kamarul Ismail, Nasir Nayan, Siti Naielah Ibrahim, Clustering is one of the important methods in data exploratory in this era because it is widely applied in data mining.Clustering of data is necessary to produce geo-demographic classification where k-means algorithm is used as cluster algorithm. K-means is one of the methods commonly used in cluster algorithm because it is more significant. However, before any data are executed on cluster analysis it is necessary to conduct some analysis to ensure the variable used in the cluster analysis is appropriate and does not have a recurring information. One analysis that needs to be done is the standardization data analysis. This study observed which standardization method was more effective in the analysis process of Malaysia’s population and housing census data for the Perak state. The rationale was that standardized data would simplify the execution of k-means algorithm. The standardized methods chosen to test the data accuracy were the z-score and range standardization method. From the analysis conducted it was found that the range standardization method was more suitable to be used for the data examined. School of Social, Development and Environmental Studies, Faculty of Social Science and Humanities, Universiti Kebangsaan Malaysia 2016-05 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/10309/1/4x.geografia-siupsi-mei16-Kamarul-edam.pdf Kamarul Ismail, and Nasir Nayan, and Siti Naielah Ibrahim, (2016) Improving the tool for analyzing Malaysia’s demographic change: data standardization analysis to form geo-demographics classification profiles using k-means algorithms. Geografia : Malaysian Journal of Society and Space, 12 (6). pp. 34-42. ISSN 2180-2491 http://www.ukm.my/geografia/v2/index.php?cont=a&item=2&thn=2016&vol=12&issue=6&ver=loc |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Kebangasaan Malaysia |
| building |
UKM Institutional Repository |
| collection |
Online Access |
| language |
English |
| description |
Clustering is one of the important methods in data exploratory in this era because it is widely applied in data
mining.Clustering of data is necessary to produce geo-demographic classification where k-means algorithm is used
as cluster algorithm. K-means is one of the methods commonly used in cluster algorithm because it is more
significant. However, before any data are executed on cluster analysis it is necessary to conduct some analysis to
ensure the variable used in the cluster analysis is appropriate and does not have a recurring information. One
analysis that needs to be done is the standardization data analysis. This study observed which standardization
method was more effective in the analysis process of Malaysia’s population and housing census data for the Perak
state. The rationale was that standardized data would simplify the execution of k-means algorithm. The standardized
methods chosen to test the data accuracy were the z-score and range standardization method. From the analysis
conducted it was found that the range standardization method was more suitable to be used for the data examined. |
| format |
Article |
| author |
Kamarul Ismail, Nasir Nayan, Siti Naielah Ibrahim, |
| spellingShingle |
Kamarul Ismail, Nasir Nayan, Siti Naielah Ibrahim, Improving the tool for analyzing Malaysia’s demographic change: data standardization analysis to form geo-demographics classification profiles using k-means algorithms |
| author_facet |
Kamarul Ismail, Nasir Nayan, Siti Naielah Ibrahim, |
| author_sort |
Kamarul Ismail, |
| title |
Improving the tool for analyzing Malaysia’s demographic change: data standardization analysis to form geo-demographics
classification profiles using k-means algorithms |
| title_short |
Improving the tool for analyzing Malaysia’s demographic change: data standardization analysis to form geo-demographics
classification profiles using k-means algorithms |
| title_full |
Improving the tool for analyzing Malaysia’s demographic change: data standardization analysis to form geo-demographics
classification profiles using k-means algorithms |
| title_fullStr |
Improving the tool for analyzing Malaysia’s demographic change: data standardization analysis to form geo-demographics
classification profiles using k-means algorithms |
| title_full_unstemmed |
Improving the tool for analyzing Malaysia’s demographic change: data standardization analysis to form geo-demographics
classification profiles using k-means algorithms |
| title_sort |
improving the tool for analyzing malaysia’s demographic change: data standardization analysis to form geo-demographics
classification profiles using k-means algorithms |
| publisher |
School of Social, Development and Environmental Studies, Faculty of Social Science and Humanities, Universiti Kebangsaan Malaysia |
| publishDate |
2016 |
| url |
http://journalarticle.ukm.my/10309/ http://journalarticle.ukm.my/10309/ http://journalarticle.ukm.my/10309/1/4x.geografia-siupsi-mei16-Kamarul-edam.pdf |
| first_indexed |
2023-09-18T19:57:03Z |
| last_indexed |
2023-09-18T19:57:03Z |
| _version_ |
1777406599698055168 |