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IJMLC 2021 Vol.11(6): 393-398 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.6.1067

Processing GIS Data Using Decision Trees and an Inductive Learning Method

Dana Mihai and Mihai Mocanu

Abstract—This paper extends recent work on spatial data mining, with another application of the classification techniques, namely with the Decision tree classifier algorithm. Spatial data mining represents a various and investigated domain because huge amounts of spatial data have been collected, ranging from remote sensing to geographical information system and computer cartography. In this work we used the Weka tool to implement the C4.5 (Quinlan) Decision tree algorithm on a dataset of Geographic Information System (GIS), data collection called Cadastre formed by a parcel plan from the Dolj district of Romania. The results of the experiments highlight several advantages and also some disadvantages of Decision tree in context of spatial data mining, with a favorable accuracy.

Index Terms—Algorithm, classification, decision tree, C4.5, Weka.

Both authors are with the University of Craiova, Romania (e-mail: mihai_danam@yahoo.com, mmocanu@software.ucv.ro).

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Cite: Dana Mihai and Mihai Mocanu, "Processing GIS Data Using Decision Trees and an Inductive Learning Method," International Journal of Machine Learning and Computing vol. 11, no. 6, pp. 393-398, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijmlc@ejournal.net


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