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
Index Terms—Algorithm, classification, decision tree, C4.5, Weka.
Both authors are with the University of Craiova, Romania (e-mail: firstname.lastname@example.org, email@example.com).
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).