An extended ID3 decision tree algorithm for spatial data
Utilizing data mining tasks such as classification on spatial data is more complex than those on non-spatial data. It is because spatial data mining algorithms have to consider not only objects of interest itself but also neighbours of the objects in order to extract useful and interesting patterns....
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| Format: | Conference or Workshop Item |
| Language: | English |
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IEEE
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/47782/ http://psasir.upm.edu.my/id/eprint/47782/ http://psasir.upm.edu.my/id/eprint/47782/1/An%20extended%20ID3%20decision%20tree%20algorithm%20for%20spatial%20data.pdf |
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eprints |
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upm-477822016-07-15T05:24:29Z http://psasir.upm.edu.my/id/eprint/47782/ An extended ID3 decision tree algorithm for spatial data Sitanggang, Imas Sukaesih Yaakob, Razali Mustapha, Norwati Nuruddin, Ahmad Ainuddin Utilizing data mining tasks such as classification on spatial data is more complex than those on non-spatial data. It is because spatial data mining algorithms have to consider not only objects of interest itself but also neighbours of the objects in order to extract useful and interesting patterns. One of classification algorithms namely the ID3 algorithm which originally designed for a non-spatial dataset has been improved by other researchers in the previous work to construct a spatial decision tree from a spatial dataset containing polygon features only. The objective of this paper is to propose a new spatial decision tree algorithm based on the ID3 algorithm for discrete features represented in points, lines and polygons. As in the ID3 algorithm that use information gain in the attribute selection, the proposed algorithm uses the spatial information gain to choose the best splitting layer from a set of explanatory layers. The new formula for spatial information gain is proposed using spatial measures for point, line and polygon features. Empirical result demonstrates that the proposed algorithm can be used to join two spatial objects in constructing spatial decision trees on small spatial dataset. The proposed algorithm has been applied to the real spatial dataset consisting of point and polygon features. The result is a spatial decision tree with 138 leaves and the accuracy is 74.72%. IEEE 2011 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/47782/1/An%20extended%20ID3%20decision%20tree%20algorithm%20for%20spatial%20data.pdf Sitanggang, Imas Sukaesih and Yaakob, Razali and Mustapha, Norwati and Nuruddin, Ahmad Ainuddin (2011) An extended ID3 decision tree algorithm for spatial data. In: 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM 2011), 29 June-1 July 2011, Fuzhou, China. (pp. 48-53). 10.1109/ICSDM.2011.5969003 |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Putra Malaysia |
| building |
UPM Institutional Repository |
| collection |
Online Access |
| language |
English |
| description |
Utilizing data mining tasks such as classification on spatial data is more complex than those on non-spatial data. It is because spatial data mining algorithms have to consider not only objects of interest itself but also neighbours of the objects in order to extract useful and interesting patterns. One of classification algorithms namely the ID3 algorithm which originally designed for a non-spatial dataset has been improved by other researchers in the previous work to construct a spatial decision tree from a spatial dataset containing polygon features only. The objective of this paper is to propose a new spatial decision tree algorithm based on the ID3 algorithm for discrete features represented in points, lines and polygons. As in the ID3 algorithm that use information gain in the attribute selection, the proposed algorithm uses the spatial information gain to choose the best splitting layer from a set of explanatory layers. The new formula for spatial information gain is proposed using spatial measures for point, line and polygon features. Empirical result demonstrates that the proposed algorithm can be used to join two spatial objects in constructing spatial decision trees on small spatial dataset. The proposed algorithm has been applied to the real spatial dataset consisting of point and polygon features. The result is a spatial decision tree with 138 leaves and the accuracy is 74.72%. |
| format |
Conference or Workshop Item |
| author |
Sitanggang, Imas Sukaesih Yaakob, Razali Mustapha, Norwati Nuruddin, Ahmad Ainuddin |
| spellingShingle |
Sitanggang, Imas Sukaesih Yaakob, Razali Mustapha, Norwati Nuruddin, Ahmad Ainuddin An extended ID3 decision tree algorithm for spatial data |
| author_facet |
Sitanggang, Imas Sukaesih Yaakob, Razali Mustapha, Norwati Nuruddin, Ahmad Ainuddin |
| author_sort |
Sitanggang, Imas Sukaesih |
| title |
An extended ID3 decision tree algorithm for spatial data |
| title_short |
An extended ID3 decision tree algorithm for spatial data |
| title_full |
An extended ID3 decision tree algorithm for spatial data |
| title_fullStr |
An extended ID3 decision tree algorithm for spatial data |
| title_full_unstemmed |
An extended ID3 decision tree algorithm for spatial data |
| title_sort |
extended id3 decision tree algorithm for spatial data |
| publisher |
IEEE |
| publishDate |
2011 |
| url |
http://psasir.upm.edu.my/id/eprint/47782/ http://psasir.upm.edu.my/id/eprint/47782/ http://psasir.upm.edu.my/id/eprint/47782/1/An%20extended%20ID3%20decision%20tree%20algorithm%20for%20spatial%20data.pdf |
| first_indexed |
2018-09-07T17:39:04Z |
| last_indexed |
2018-09-07T17:39:04Z |
| _version_ |
1610971291389526016 |