Affiliations: [a] Graduate School, University of Chinese Academy of Social Sciences, Beijing 102488, China | [b] School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China | [c] Institute of Quantitative and Technical Economics, Chinese Academy of Social Sciences, Beijing 100732, China
Correspondence:
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Corresponding authors: Yiyan Chen, School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China. E-mail: [email protected].
Note: [1] These authors contributed equally to this work and should be considered co-first authors.
Abstract: If we endow an intelligent system with fuzzy logic, we hope that it can deal with fuzzy data, including the clustering of fuzzy data. This paper proposes a fuzzy mixed data clustering algorithm by fast search and find of density peaks (FMTD-CFSFDP), which is a development of the CFSFDP clustering algorithm. The proposed algorithm is a kind of density-based clustering method established using fuzzy sets for fuzzy mixed data. Mathematical definitions for fuzzy mixed data are presented. Combined with the definition of traditional fuzzy Euclidean distance, we defined an improved Euclidean distance for both continuous and discrete fuzzy sets with smaller error. On this basis, the weight between continuous and discrete indicators is introduced for establishing the global difference for fuzzy mixed data. Referring to the clustering procedures of the CFSFDP algorithm, a Gaussian Kernel function for fuzzy samples is calculated and the clustering procedures of our proposed algorithm are described in detail. Furthermore, four different sets of random simulations are performed, which illustrates the feasibility of the proposed algorithm.
