Affiliations: Research Center for Secure Application in Networks and
Communications Southwest Jiaotong University, Chengdu 610031, China.
E-mail: songj@s-ec.com | School of Information Science and Technology Southwest
Jiaotong University, Chengdu 610031, China. E-mail: trli@swjtu.edu.cn | Belgian Nuclear Research Centre (SCK·CEN),
Boeretang 200, 2400 Mol, Belgium and Department of Applied Mathematics &
Computer Science Ghent University, B-9000 Gent, Belgium. E-mail: druan@sckcen.be;
da.ruan@ugent.be
Abstract: 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.
Keywords: Rough set theory, cloud transform, decision trees, weighted mean roughness, characteristic relation
