TASC: Two-attribute-set clustering through decision tree construction |
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| Institution: | 1. Charles Sturt University, Albury, NSW 2640, Australia;2. University of Technology Sydney, Ultimo, NSW 2007, Australia;1. The Big Data Research Center, the School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, PR China;2. The Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 611731, PR China;1. Preparatory School of Science and Technology, Annaba, P.O. Box 218, 23000, Algeria;2. Electronic Document Management Laboratory (LabGED), Badji Mokhtar-Annaba University, P.O. Box 12 Annaba, Algeria;3. Real Time Intelligent System Laboratory, University of Nevada, Las Vegas, NV 89154, USA;4. Department of Computer Science, University of Quebec At Montreal, PO box 8888, Downtown station, Montreal (Quebec) Canada, H3C 3P8, Canada;5. University of Lorraine, LORIA, Campus Scientifique, BP 239, 54506 Vandoeuvre-lès-Nancy, France;1. School of Electronic Engineering, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, Shaanxi Province 710071, China;2. School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China |
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| Abstract: | Clustering is the process of grouping a set of objects into classes of similar objects. In the past, clustering algorithms had a common problem that they use only one set of attributes for both partitioning the data space and measuring the similarity between objects. This problem has limited the use of the existing algorithms on some practical situation. Hence, this paper introduces a new clustering algorithm, which partitions data space by constructing a decision tree using one attribute set, and measures the degree of similarity using another. Three different partitioning methods are presented. The algorithm is explained with illustration. The performance and accuracy of the four partitioning methods are evaluated and compared. |
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