dc.contributor.author | Sitanggang, Imas Sukaesih | |
dc.contributor.author | Yaakob, Razali | |
dc.contributor.author | Mustapha, Norwati | |
dc.contributor.author | Ainuddin B Nuruddin, Ahmad | |
dc.date.accessioned | 2016-06-13T03:50:15Z | |
dc.date.available | 2016-06-13T03:50:15Z | |
dc.date.issued | 2011 | |
dc.identifier.isbn | 978-1-4244-8349-5 | |
dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/81044 | |
dc.description.abstract | 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%. | id |
dc.language.iso | en | id |
dc.publisher | Institute of Electrical and Electronics Engineers, Inc. | id |
dc.title | An Extended ID3 Decision Tree Algorithm for Spatial Data | id |
dc.type | Article | id |
dc.subject.keyword | ID3 algorithm | id |
dc.subject.keyword | spatial decision tree | id |
dc.subject.keyword | spatial information gain | id |
dc.subject.keyword | spatial relation | id |
dc.subject.keyword | spatial measure | id |