We present a novel method for the construction of decision trees. The method utilises concept lattices in that certain formal concepts of the concept lattice associated to input data are used as nodes of the decision tree constructed from the data. The concept lattice provides global information about natural clusters in the input data, which we use for selection of splitting attributes. The usage of such global information is the main novelty of our approach. Experimental evaluation indicates good performance of our method. We describe the method, experimental results, and a comparison with standard methods on benchmark datasets.
['Radim Belohlavek', 'Bernard De Baets', 'Jan Outrata', 'Vilem Vychodil']
Inducing decision trees via concept lattices