메뉴 건너뛰기




Volumn 246, Issue , 2013, Pages 133-154

Fuzzy partition based soft subspace clustering and its applications in high dimensional data

Author keywords

Convergence; Fuzzy clustering; High dimensional data; Soft subspace clustering

Indexed keywords

CLUSTERING TECHNIQUES; CONVERGENCE; CONVERGENCE THEOREM; HIGH DIMENSIONAL DATA; HIGH-DIMENSIONAL; ITS APPLICATIONS; OBJECTIVE FUNCTIONS; SOFT SUBSPACES;

EID: 84879780191     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2013.05.029     Document Type: Article
Times cited : (39)

References (46)
  • 4
    • 0036120047 scopus 로고    scopus 로고
    • Projective ART for clustering data sets in high dimensional spaces
    • Y. Cao, and J. Wu Projective ART for clustering data sets in high dimensional spaces Neural Networks 15 2002 105 120
    • (2002) Neural Networks , vol.15 , pp. 105-120
    • Cao, Y.1    Wu, J.2
  • 5
    • 1842762839 scopus 로고    scopus 로고
    • An optimization algorithm for clustering using weighted dissimilarity measures
    • Y. Chan, W. Ching, M.K. Ng, and J.Z. Huang An optimization algorithm for clustering using weighted dissimilarity measures Pattern Recognition 37 5 2004 943 952
    • (2004) Pattern Recognition , vol.37 , Issue.5 , pp. 943-952
    • Chan, Y.1    Ching, W.2    Ng, M.K.3    Huang, J.Z.4
  • 6
    • 70449699648 scopus 로고    scopus 로고
    • Enhanced soft subspace clustering integrating within-cluster and between-cluster information
    • Z. Deng, K. Choi, F.L. Chung, and S. Wang Enhanced soft subspace clustering integrating within-cluster and between-cluster information Pattern Recognition 43 3 2010 767 781
    • (2010) Pattern Recognition , vol.43 , Issue.3 , pp. 767-781
    • Deng, Z.1    Choi, K.2    Chung, F.L.3    Wang, S.4
  • 7
    • 33749402881 scopus 로고    scopus 로고
    • A fuzzy subspace algorithm for clustering high dimensional data
    • X. Li, O. Zaiane, Z. Li, Springer Berlin
    • G. Gan, J. Wu, and Z. Yang A fuzzy subspace algorithm for clustering high dimensional data X. Li, O. Zaiane, Z. Li, Lecture Notes in Artificial Intelligence vol. 4093 2006 Springer Berlin 271 278
    • (2006) Lecture Notes in Artificial Intelligence , vol.4093 , pp. 271-278
    • Gan, G.1    Wu, J.2    Yang, Z.3
  • 8
    • 38949102340 scopus 로고    scopus 로고
    • A convergence theorem for the fuzzy subspace clustering (FSC) algorithm
    • G. Gan, and J. Wu A convergence theorem for the fuzzy subspace clustering (FSC) algorithm Pattern Recognition 41 2008 1939 1947
    • (2008) Pattern Recognition , vol.41 , pp. 1939-1947
    • Gan, G.1    Wu, J.2
  • 9
    • 0023592114 scopus 로고
    • An improved convergence theorem for the fuzzy c-means clustering algorithms
    • J. Bezdek, CRC Press Boca Raton
    • R. Hathaway, J. Bezdek, and W. Tucker An improved convergence theorem for the fuzzy c-means clustering algorithms J. Bezdek, Analysis of Fuzzy Information vol. III 1987 CRC Press Boca Raton 123 131
    • (1987) Analysis of Fuzzy Information , vol.3 , pp. 123-131
    • Hathaway, R.1    Bezdek, J.2    Tucker, W.3
  • 12
    • 77950369345 scopus 로고    scopus 로고
    • Data clustering: 50 years beyond k-means
    • A.K. Jain Data clustering: 50 years beyond k-means Pattern Recognition Letters 31 8 2010 651 666
    • (2010) Pattern Recognition Letters , vol.31 , Issue.8 , pp. 651-666
    • Jain, A.K.1
  • 14
    • 34347228671 scopus 로고    scopus 로고
    • An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data
    • Liping Jing, Michael Ng, and Joshua Huang An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data IEEE Transactions on Knowledge and Data Engineering 19 8 2007 1026 1041
    • (2007) IEEE Transactions on Knowledge and Data Engineering , vol.19 , Issue.8 , pp. 1026-1041
    • Jing, L.1    Ng, M.2    Huang, J.3
  • 15
    • 0346094167 scopus 로고    scopus 로고
    • Simple Gabor feature space for invariant object recognition
    • V. Kyrki, J.K. Kamarainen, and H. Kalviainen Simple Gabor feature space for invariant object recognition Pattern Recognition Letter 25 3 2004 311 318
    • (2004) Pattern Recognition Letter , vol.25 , Issue.3 , pp. 311-318
    • Kyrki, V.1    Kamarainen, J.K.2    Kalviainen, H.3
  • 16
    • 33244495420 scopus 로고    scopus 로고
    • Towards a robust clustering
    • J.M. Leski Towards a robust clustering Fuzzy Sets and Systems 137 2 2003 191 196
    • (2003) Fuzzy Sets and Systems , vol.137 , Issue.2 , pp. 191-196
    • Leski, J.M.1
  • 17
    • 0036681018 scopus 로고    scopus 로고
    • A fuzzy algorithm for color quantization of images
    • D. Özdemir, and L. Akarun A fuzzy algorithm for color quantization of images Pattern Recognition 35 8 2002 1785 1791
    • (2002) Pattern Recognition , vol.35 , Issue.8 , pp. 1785-1791
    • Özdemir, D.1    Akarun, L.2
  • 18
    • 84950632109 scopus 로고
    • Objective criteria for the evaluation of clustering methods
    • W.M. Rand Objective criteria for the evaluation of clustering methods Journal of the American Statistical Association 66 1971 846 850
    • (1971) Journal of the American Statistical Association , vol.66 , pp. 846-850
    • Rand, W.M.1
  • 21
    • 33244486981 scopus 로고    scopus 로고
    • Robust maximum entropy clustering algorithm with its labeling for outliers
    • S. Wang, F.L. Chung, Z.H. Deng, D.W. Hu, and X. Wu Robust maximum entropy clustering algorithm with its labeling for outliers Soft Computing 10 7 2006 555 563
    • (2006) Soft Computing , vol.10 , Issue.7 , pp. 555-563
    • Wang, S.1    Chung, F.L.2    Deng, Z.H.3    Hu, D.W.4    Wu, X.5
  • 24
    • 28444491389 scopus 로고    scopus 로고
    • On discovery of extremely low-dimensional clusters using semi-supervised projected clustering
    • K.Y. Yip, D.W. Cheung, M.K. Ng, On discovery of extremely low-dimensional clusters using semi-supervised projected clustering, in: Proc. 21st Int'l Conf. Data Eng., 2005, pp. 329-340.
    • (2005) Proc. 21st Int'l Conf. Data Eng. , pp. 329-340
    • Yip, K.Y.1    Cheung, D.W.2    Ng, M.K.3
  • 25
    • 14644432442 scopus 로고    scopus 로고
    • Optimality test for generalized FCM and its application to parameter selection
    • J. Yu, and M.S. Yang Optimality test for generalized FCM and its application to parameter selection IEEE Transactions on Fuzzy Systems 13 1 2005 164 176
    • (2005) IEEE Transactions on Fuzzy Systems , vol.13 , Issue.1 , pp. 164-176
    • Yu, J.1    Yang, M.S.2
  • 27
    • 84879780696 scopus 로고    scopus 로고
    • ftp://www.cs.umn.edu/karypis/CLUTO/files/datasets.tar.gz.
  • 28
    • 84879779868 scopus 로고    scopus 로고
    • UCI KDD Archive. 2005.9
    • UCI KDD Archive. < http://kdd.ics.uci.edu/databases/20newsgroups >, 2005.9.
  • 31
    • 2942534051 scopus 로고    scopus 로고
    • Improving fuzzy c-means clustering based on feature-weight learning
    • Xizhao Wang, Yadong Wang, and Lijuan Wang Improving fuzzy c-means clustering based on feature-weight learning Pattern Recognition Letters 25 10 2004 1123 1132
    • (2004) Pattern Recognition Letters , vol.25 , Issue.10 , pp. 1123-1132
    • Wang, X.1    Wang, Y.2    Wang, L.3
  • 33
    • 83755194773 scopus 로고    scopus 로고
    • A comparative experimental study of feature-weight learning approaches
    • Hong-Jie Xing, Xizhao Wang, and Minghu Ha A comparative experimental study of feature-weight learning approaches SMC 2011 3500 3505
    • (2011) SMC , pp. 3500-3505
    • Xing, H.-J.1    Wang, X.2    Ha, M.3
  • 35
    • 84879781753 scopus 로고    scopus 로고
    • Research on subspace possibilistic clustering mechanism
    • (in Chinese)
    • Qing Guan, Zhao-hong Deng, and Shi-tong Wang Research on subspace possibilistic clustering mechanism Computer Engineering 37 5 2011 224 226 (in Chinese)
    • (2011) Computer Engineering , vol.37 , Issue.5 , pp. 224-226
    • Guan, Q.1    Deng, Z.-H.2    Wang, S.-T.3
  • 36
    • 0030214781 scopus 로고    scopus 로고
    • The possibilistic C-Means algorithm: Insights and recommendations
    • R. Krishnapuram, and J.M. Keller The possibilistic C-Means algorithm: Insights and recommendations IEEE Transactions on Fuzzy Systems 4 3 1996 385 393
    • (1996) IEEE Transactions on Fuzzy Systems , vol.4 , Issue.3 , pp. 385-393
    • Krishnapuram, R.1    Keller, J.M.2
  • 38
    • 0042312608 scopus 로고    scopus 로고
    • Feature weighting in k-means clustering
    • Dharmendra S. Modha, and W. Scott Spangler Feature weighting in k-means clustering Machine Learning 52 3 2003 217 237
    • (2003) Machine Learning , vol.52 , Issue.3 , pp. 217-237
    • Modha, D.S.1    Scott Spangler, W.2
  • 39
    • 79953232758 scopus 로고    scopus 로고
    • An entropy weighting mixture model for subspace clustering of high-dimensional data
    • Liuqing Peng, and Junying Zhang An entropy weighting mixture model for subspace clustering of high-dimensional data Pattern Recognition Letters 32 8 2011 1154 1161
    • (2011) Pattern Recognition Letters , vol.32 , Issue.8 , pp. 1154-1161
    • Peng, L.1    Zhang, J.2
  • 41
    • 10744221429 scopus 로고    scopus 로고
    • A contribution to convergence theory of fuzzy c-means and derivatives
    • Frank Hoppner, and Frank Klawonn A contribution to convergence theory of fuzzy c-means and derivatives IEEE Transactions on Fuzzy Systems 11 5 2003 682 694
    • (2003) IEEE Transactions on Fuzzy Systems , vol.11 , Issue.5 , pp. 682-694
    • Hoppner, F.1    Klawonn, F.2
  • 42
    • 79952314931 scopus 로고    scopus 로고
    • An agglomerative clustering algorithm using a dynamic k-nearest-neighbor list
    • Jim Z.C. Lai, and Tsung-Jen Huang An agglomerative clustering algorithm using a dynamic k-nearest-neighbor list Information Sciences 181 9 2011 1722 1734
    • (2011) Information Sciences , vol.181 , Issue.9 , pp. 1722-1734
    • Lai, J.Z.C.1    Huang, T.-J.2
  • 43
    • 81355146456 scopus 로고    scopus 로고
    • A fuzzy minimax clustering model and its applications
    • Xiang Li, Hau-San Wong, and Si Wu A fuzzy minimax clustering model and its applications Information Sciences 186 1 2012 114 125
    • (2012) Information Sciences , vol.186 , Issue.1 , pp. 114-125
    • Li, X.1    Wong, H.-S.2    Wu, S.3
  • 45
    • 80055042560 scopus 로고    scopus 로고
    • A clustering algorithm for multiple data streams based on spectral component similarity
    • Ling Chen, Ling-Jun Zou, and Li Tu A clustering algorithm for multiple data streams based on spectral component similarity Information Sciences 183 1 2012 35 47
    • (2012) Information Sciences , vol.183 , Issue.1 , pp. 35-47
    • Chen, L.1    Zou, L.-J.2    Tu, L.3
  • 46
    • 78751648727 scopus 로고    scopus 로고
    • Fuzzy clustering of time series in the frequency domain
    • E.A. Maharaj, and P. D'Urso Fuzzy clustering of time series in the frequency domain Information Sciences 181 7 2011 1187 1211
    • (2011) Information Sciences , vol.181 , Issue.7 , pp. 1187-1211
    • Maharaj, E.A.1    D'Urso, P.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.