A clustering algorithm for fuzzy numbers based on fast search and find of density peaks

摘要

This paper made improvements on clustering by fast search and find of density peaks (CFSFDP) algorithm and extended this algorithm to fuzzy numbers (FNCFSFDP algorithm). Using FNCFSFDP 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 cutoff 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 FNCFSFDP 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.