Agglomerative clustering using improved rough sets and its applications in cooperative object localization

Authors:
Wei Chen;Zhibo Tang;Xiaorong Jiang;Jing Gao;Renke Sun;Sanad Hashlan
Affiliations:
College of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China and State Key Laboratory of Coal Resources and Safe Mining, China University of ...;College of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China;College of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China;College of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China;College of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China;Computer Science Department, Faculty of Computing and Information Technology, King AbdulAziz University, Jeddah 21589, Saudi Arabia
Venue:
Computers and Electrical Engineering
Year:
2013

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Abstract

Rough set has been applied to extract knowledge from various types of databases. Some limitations have been discovered in rough set, such as label inconsistency, the lack of flexibility and excessive dependency on discretization of the initial attributes. To overcome these limitations, a novel agglomerative clustering method using improved rough set is proposed. The idea of using equivalence class was also incorporated to merge and divide subclass. The experimental applications in data extraction and cooperative object localization showed the effectiveness of the presented improved rough set combined with agglomerative clustering.