A Two-Stage Approach for Constructing Type-2 Information Granules

作     者：Zhu, Xiubin Pedrycz, Witold Li, Zhiwu 

作者机构：Xidian Univ Sch Electromech Engn Xian 710071 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada King Abdulaziz Univ Fac Engn Jeddah 21589 Saudi Arabia Macau Univ Sci & Technol Inst Syst Engn Taipa Macao Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON CYBERNETICS》 (IEEE Trans. Cybern.)

年 卷 期：2022年第52卷第4期

页      面：2214-2224页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [61472295, 61672400] Recruitment Program of Global Experts Science and Technology Development Fund, Macau Special Administrative Region (MSAR) [0012/2019/A1] National Key Research and Development Program of China [2018YFB1700104] Guangxi Key Laboratory of Trusted Software [kx201926] 

主　　题：Collaboration Clustering algorithms Prototypes Partitioning algorithms Clustering methods Cybernetics Topology Collaborative clustering granular computing (GrC) principle of justifiable granularity type-2 information granules 

摘      要：In this article, we are concerned with the formation of type-2 information granules in a two-stage approach. We present a comprehensive algorithmic framework which gives rise to information granules of a higher type (type-2, to be specific) such that the key structure of the local granular data, their topologies, and their diversities become fully reflected and quantified. In contrast to traditional collaborative clustering where local structures (information granules) are obtained by running algorithms on the local datasets and communicating findings across sites, we propose a way of characterizing granular data (formed) by forming a suite of higher type information granules to reveal an overall structure of a collection of locally available datasets. Information granules built at the lower level on a basis of local sources of data are weighted by the number of data they represent while the information granules formed at the higher level of hierarchy are more abstract and general, thus facilitating a formation of a hierarchical description of data realized at different levels of detail. The construction of information granules is completed by resorting to fuzzy clustering algorithms (more specifically, the well-known Fuzzy C-Means). In the formation of information granules, we follow the fundamental principle of granular computing, viz., the principle of justifiable granularity. Experimental studies concerning selected publicly available machine-learning datasets are reported.