C4.5: programs for machine learning
C4.5: programs for machine learning
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Classification of meteorological volumetric radar data using rough set methods
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Artificial Intelligence with Uncertainty
Artificial Intelligence with Uncertainty
Attribute reduction in decision-theoretic rough set models
Information Sciences: an International Journal
A new decision tree construction using the cloud transform and rough sets
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Characteristic relations for incomplete data: a generalization of the indiscernibility relation
Transactions on Rough Sets IV
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Decision trees are one of the most popular data-mining techniques for knowledge discovery. Many approaches for induction of decision trees often deal with the continuous data and missing values in information systems. However, they do not perform well in real situations. This paper presents a new algorithm, decision tree construction based on the Cloud transform and Rough set theory under the characteristic relation (CR), for mining classification knowledge from a given data set. The continuous data is transformed into discrete qualitative concepts via the cloud transformation and then the attribute with the smallest weighted mean roughness under the characteristic relation is selected as the current splitting node. Experimental evaluation shows the decision trees constructed by the CR algorithm tend to have a simpler structure, much higher classification accuracy and more understandable rules than those by C5.0 in most cases.