Computer Science ›› 2021, Vol. 48 ›› Issue (1): 145-151.doi: 10.11896/jsjkx.200400043

• Database & Big Data & Data Science • Previous Articles     Next Articles

Weighted Hesitant Fuzzy Clustering Based on Density Peaks

ZHANG Yu, LU Yi-hong, HUANG De-cai   

  1. College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2020-04-10 Revised:2020-08-08 Online:2021-01-15 Published:2021-01-15
  • About author:ZHANG Yu,born in 1994,postgra-duate,is a member of China Computer Federation.Her main research interests include data mining and so on.
    LU Yi-hong,born in 1968,master,associate professor,is a member of China Computer Federation.Her main research interests include software theory and data mining.
  • Supported by:
    Zhejiang Public Welfare Technology Research Project(LGG19E090001).

Abstract: Due to cognitive limitations and the information uncertainty,traditional fuzzy clustering cannot effectively solve the decision-making problems in a real-life scenario when cluster analysis is carried out on the decision problem.Therefore,hesitant fuzzy sets(HFSs) clustering algorithms were proposed.The conception of hesitant fuzzy sets is evolved from fuzzy sets which are applied to fuzzy linguistic approach.The distance function of the hierarchical hesitant fuzzy K-means clustering algorithm has the same weight since the datasets information is seldom considered,and the computational complexity for computing the cluster center is exponential which is unavailable in the big data environment.In order to solve the above problems,this paper presents a novel clustering algorithm for hesitant fuzzy sets based on density peaks,called WHFDP.Firstly,a new method for extending the short hesitant fuzzy elements set to calculate the distance between two HFSs is proposed and a new formula for calculating the weight of distance function combined with the coefficient of variation is given.In addition,the computational complexity for computing the cluster center is reduced by using density peaks clustering method to select cluster center.Meanwhile,the adaptability to data sets with different sizes and arbitrary shapes is also improved.The time complexity and space complexity of the algorithm are reduced to polynomial level.Finally,typical data sets are used for simulation experiments,which prove the effectiveness of the new algorithm.

Key words: Clustering algorithm, Coefficient of variation, Data mining, Density peaks, Hesitant fuzzy sets

CLC Number: 

  • TP391
[1] XIA Z H,WANG X H,SUN X M,et al.Steganalysis of LSB matching using differences between nonadjacent pixels[J].Multimedia Tools and Applications,2016,75(4):1947-1962.
[2] ANWAR T,LIU C F,VU H L,et al.Partit-ioning road networks using density peak graphs:Efficiency vs.accuracy[J].Information Systems,2017,64(C):22-40.
[3] AHN C S,OH S Y.Robust vocabulary reco-gnition clustering model using an average estimator least mean square filter in noisy environments[J].Personal & Ubiquitous Computing,2013,18(6):1295-1301.
[4] JIN J G.Review of clustering method[J].Computer Science,2014,41(11A):288-293.
[5] STREHL A,GHOSH J.Cluster ensembles:a knowledge reuse framework for combining partitionings[J].Journal of Machine Learning Research,2002,3(3):583-617.
[6] FRED A L N,JAIN A K.Combining multiple clusterings using evidence accumulation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(6):835-850.
[7] IAMON N,BOONGOEN T,GARRETT S,et al.A link-based cluster ensemble approach for categorical data clustering[J].IEEE Transactions on Knowledge & Data Engineering,2012,24(3):413-425.
[8] TANG W,ZHOU Z H.Bagging-based selective clusterer ensemble[J].Journal of Software,2005,16(4):496-502.
[9] HUANG D,WANG C D,WU J S,et al.Ultra-Scalable Spectral Clustering and Ensemble Clustering[J].IEEE Transactions on Knowledge and Data Engineering,2020,32(6):1212-1226.
[10] ZADEH L A.The concept of a linguistic variable and its application to approximate reasoning[J].Information Science,1975,8(3):199-249.
[11] LIAO H C,XU Z S,ZENG X J,et al.Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets[J].Knowledge Based Systems,2015,76:127-138.
[12] MENG F,CHEN X,ZHANG Q.Multi-attribute decision analysis under a linguistic hesitant fuzzy environment[J].Information Sciences,2014,267:287-305.
[13] YAVUZ M,OZTAYSI B,ONAR S C,et al.Multi-criteria evaluation of alternative-fuel vehicles via a hierarchical hesitant fuzzy linguistic model[J].Expert Systems with Applications,2015,42(5):2835-2848.
[14] ATANASSOV K T.Intuitionistic fuzzy sets[J].Fuzzy Sets and Systems,1986,20(1):87-96.
[15] MIZUMOTO M,TANAKA K.Some properties of fuzzy sets of type 2[J].Information and Control,1976,31(4):312-340.
[16] TORRA V,NARUKAWA Y.On Hesitant fuzzy sets and decision[C]//IEEE International Conference on Fuzzy Systems.2009:1378-1382.
[17] YAGE R,RONALD R.On the theory of bags[J].International Journal of General System,1986,13(1):23-37.
[18] MIYAMOTO S.Information clustering based on fuzzy multisets[J].Information Processing and Management,2003,39(2):195-213.
[19] YAO D B,WANG C C.Hesitant intuitionistic fuzzy entropy/cross-entropy and their applications[J].Soft Computing,2018,22(9):2809-2824.
[20] CHEN NA,XU Z S,XIA M M.Hierarchical hesitant fuzzy K-means clustering algorithm[J].Applied Mathematics-A Journal of Chinese Universities,2014,29(1):1-17.
[21] XIA M M,XU Z S.Hesitant fuzzy information aggregation in decision making[J].International Journal of ApproximateReasoning,2011,52(3):395-407.
[22] MERIGO J M,CASANOVAS M.Induced aggregation operators in decision making with the Dempster-Shafer belief structure[J].International Journal of Intelligent Systems,2009,24(8):934-954.
[23] LIU H W,WANG G J.Multi-criteria decision making methods based on intuitionistic fuzzy sets[J].European Journal of Operational Research,2007,179(1):220-233.
[24] XU Z S,XIA M M.Distance and similarity measures for hesitant fuzzy sets[J].Information Science,2011,181(11):2128-2138.
[25] RODRIGUEZ A,LAIO A.Clustering by fast search and find of density peaks[J].Science,2014,344(6191):1492-1496.
[26] DU M J,DING S F,JIA H.Study on density peaks clustering based on k-nearest neighbors and principal component analysis[J].Knowledge Based Systems,2016,99:135-145.
[27] XIE J Y,GAO H C,LIU X,et al.Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors[J].Information Sciences,2016,354(c):19-40.
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