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 author: Yiyan Chen, School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China. E-mail: townjam_sovietnia@163.com.
Note: [1] These authors contributed equally to this work and should be considered as co-first authors.
Abstract: This paper made improvements on clustering by fast search and find of density peaks (CFSFDP) algorithm and extended this algorithm to fuzzy numbers (FN-CFSFDP algorithm). Using FN-CFSFDP algorithm, classical information included in the samples are extended to fuzzy sets, and fuzzy samples can be clustered by searching the density peak. Firstly, by means of error analysis, improved Euclidean distance between fuzzy numbers was defined, and some key parameters or operating quantities mainly including cut-off distance and Gaussian Kernel function of fuzzy samples were introduced in detail. Next, 76 random simulations in total were performed on four sets of samples under different conditions with different t-values, different sample sizes, index numbers, cluster numbers and fetching rules. Moreover, Kappa coefficients in above simulations were calculated. Finally, both advantages and disadvantages of the proposed FN-CFSFDP were concluded and some recommendations for improvement were put forward, which can provide insightful guidance for further investigations of fuzzy clustering algorithms on fuzzy sets.
