基于空间距离自适应权重度量的粗糙K-means算法

doi:10.11896／j.issn.1002-137X.2018.07.033

Abstract

Abstract: The setting of weights coefficient of lower approximation and boundary area in rough K-means algorithm has an important influence on final clustering results of algorithm.However,traditional rough K-means and many refined rough K-means algorithms set up fixed weights of lower approximations and boundary area for all clusters,ignoring the effect of distribution difference of data objects within clusters.To cope with this problem,a new rough K-means algorithm with self-adaptive weights measurement based on space distance was proposed according to the spatial distribution of objects in lower approximation and boundary area relative to the cluster centers.During each iteration process,diffe-rent importance of lower approximation and boundary area on iterative computation of cluster centers was measured based on average distance of objects in lower approximation and boundary area relative to cluster centers and the relative weights coefficient of lower approximation and boundary area were dynamically calculated.The validity of the algorithm was verified by experimental analysis.

Key words: Clustering algorithm, Rough k-means, Rough set, Self-adaptive weight

CLC Number:

TP391

WANG Hui-yan,ZHANG Teng-fei,MA Fu-min. Rough K-means Algorithm with Self-adaptive Weights Measurement Based on Space Distance[J].Computer Science, 2018, 45(7): 190-196.

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References

[1]HAN J,KAMBER M.Data mining,concepts and techniques(third edition) [M].California:Morgan Kaufmann Publishers,2011.
[2]HARTIGAN J A,WONG M A.A K-Means Clustering Algorithm [J].Journal of the Royal Statistical Society,1979,28(1):100-108.
[3]AMORIM R C D.A Survey on Feature Weighting BasedK-Means Algorithms[J].Journal of Classification,2016,33(2):210-242.
[4]KHANDARE A,ALVI A S.Survey of Improved k-means Clustering Algorithms:Improvements,Shortcomings and Scope for Further Enhancement and Scalability[C]∥Information Systems Design and Intelligent Applications.New Delhi:Springer,2016:495-503.
[5]BEZDEK J C.Fuzzy Mathematics in Pattern Classification[D].Ithaca:Cornell University,1973.
[6]BEZDEK J C.Pattern Recognition with Fuzzy Objective function Algorithms [M].New York:Plenum Press,1981.
[7]JIANG Z H,LI T T,MIN W F,et al.Fuzzy c-means clustering based on weights and gene expression programming [J].Pattern Recognition Letters,2017,90:1-7.
[8]CHEN H P,SHEN X J,LV Y D,et al.A novel automatic fuzzy clustering algorithm based on softpartition and membership information [J].Neurocomputing,2017,236(SI):104-112.
[9]KRISHNAPURAM R,KELLER J M.A Possibilistic Approach to Clustering [J].IEEE Transactions on Fuzzy Systems,1993,1(2):98-110.
[10]KRISHNAPURAM R,KELLER J M.The Possibilistic C-Means Algorithm:Insights and Recommendations [J].IEEE Transactions on Fuzzy Systems,1996,4(3):385-393.
[11]BARNIM,CAPPELLINI V,MECOCCI A.Comments on A Possibilistic Approach to Clustering [J].IEEE Transactions on Fuzzy Systems,1996,4(3):393-396.
[12]LINGRAS P,WEST C.Interval Set Clustering of Web Users with Rough K-Means [J].Journal of Intelligent Information Systems,2004,23(1):5-16.
[13]PAWLAK Z.Rough Sets[J].International Journal of Computer &Information Sciences,1982,11(5):341-356.
[14]MAJI P,PAL S K.RFCM:A Hybrid Clustering AlgorithmUsing Rough and Fuzzy Sets[J].Fundamenta Informaticae,2007,80(4):475-496.
[15]MITRA S,BANKA H,PEDRYCZ W.Rough-Fuzzy Collabora-tive Clustering [J].IEEE Transactions on Systems Manand Cybernetics Part B Cybernetics,2006,36(4):795-805.
[16]PAUL S,MAJI P.A New Rough-Fuzzy Clustering Algorithmand its Applications [C]∥Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012).New Delhi:Springer,2014:1245-1251.
[17]SHI J,LEI Y,ZHOU Y,et al.Enhanced rough-fuzzy c-means algorithm with strict rough setsproperties [J].Applied Soft Computing,2016,46:827-850.
[18]ZHANG T F,CHEN L,MA F M.A modified rough c-meansclustering algorithm based on hybrid imbalanced measureof distance and density[J].International Journal of Approximate Reasoning,2014,55(8):1805-1818.
[19]WANG H,ZHOU M.A refined rough k-means clustering with hybrid threshold [C]∥International Conference on Rough Sets and Current Trends in Computing.Berlin Heidelberg:Springer,2012:26-35.
[20]MALYSZKO D,STEPANIUK J.Rough Entropy Based k-Means Clustering [C]∥Rough Sets,Fuzzy Sets,Data Mining and Granular Computing.Berlin Heidelberg:Springer,2009:406-413.
[21]PAL S K,SHANKAR B U,MITRA P.Granular computing,rough entropy and object extraction[J].Pattern Recognition Letters,2005,26(16):2509-2517.
[22]PETERS G.Rough Clustering Utilizing the Principle of Indiffe-rence [J].Information Sciences,2014,277(2):358-374.
[23]WANG X E,HAN D Q,HAN C Z.Selection method for Para-meters of Rough Fuzzy C-Means Clustering Based on Uncertainty Measurement[J].Journal of Xi’an Jiaotong University,2013,47(6):55-60.(in Chinese)
王学恩,韩德强,韩崇昭.采用不确定性度量的粗糙模糊C均值聚类参数获取方法[J].西安交通大学学报,2013,47(6):55-60.
[24]PETERS G.Some refinements of rough k-means clustering [J].Pattern Recognition,2006,39(8):1481-1491.
[25]BEZDEK J C,PAL N R.Some New Indexes of Cluster Validity [J].IEEE Transactions on System Manand Cybernetics Part B Cybernetics,1988,28(3):301-315.

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Rough K-means Algorithm with Self-adaptive Weights Measurement Based on Space Distance

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