메뉴 건너뛰기




Volumn 32, Issue 7, 2011, Pages 1062-1069

A k-means type clustering algorithm for subspace clustering of mixed numeric and categorical datasets

Author keywords

Categorical data; Clustering; Mixed data; Subspace clustering

Indexed keywords

CATEGORICAL DATA; CATEGORICAL DATASETS; CLUSTER FORMATIONS; CLUSTERING; CLUSTERING RESULTS; DATA POINTS; DATA SETS; K-MEANS; MIXED DATA; REAL DATA SETS; SUBSPACE CLUSTERING;

EID: 79952545400     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2011.02.017     Document Type: Article
Times cited : (51)

References (24)
  • 1
    • 0039253822 scopus 로고    scopus 로고
    • Finding generalized projected clusters in high dimensional spaces
    • Aggarwal, C.C.; Yu, P.S.; 2000. Finding generalized projected clusters in high dimensional spaces, In: Proc. ACM SIGMOD.
    • (2000) Proc. ACM SIGMOD
    • Aggarwal, C.C.1    Yu, P.S.2
  • 2
    • 0032090765 scopus 로고    scopus 로고
    • Automatic subspace clustering of high dimensional data for data mining applications
    • Agrawal, R.; Gehrke, Gunopulos, J. D.; Raghavan, P. 1998. Automatic subspace clustering of high dimensional data for data mining applications. In: Proc. 1998 ACM SIGMOD, 94-105. (Pubitemid 128655960)
    • (1998) SIGMOD Record , vol.27 , Issue.2 , pp. 94-105
    • Agrawal, R.1    Gehrke, J.2    Gunopulos, D.3    Raghavan, P.4
  • 3
    • 33750473714 scopus 로고    scopus 로고
    • A method to compute distance between two categorical values of same attribute in unsupervised learning for categorical data set
    • DOI 10.1016/j.patrec.2006.06.006, PII S0167865506001759
    • A. Ahmad, and L. Dey A method to compute distance between two categorical values of same attribute in unsupervised learning for categorical data set Pattern Recognit. Lett. 28 1 2007 110 118 (Pubitemid 44646671)
    • (2007) Pattern Recognition Letters , vol.28 , Issue.1 , pp. 110-118
    • Ahmad, A.1    Dey, L.2
  • 4
    • 34447330447 scopus 로고    scopus 로고
    • A k-mean clustering algorithm for mixed numeric and categorical data
    • DOI 10.1016/j.datak.2007.03.016, PII S0169023X0700050X
    • A. Ahmad, and L. Dey A k-mean clustering algorithm for mixed numeric and categorical data Data Knowl. Eng. 63 2 2007 503 527 (Pubitemid 47053940)
    • (2007) Data and Knowledge Engineering , vol.63 , Issue.2 , pp. 503-527
    • Ahmad, A.1    Dey, L.2
  • 5
    • 0038494682 scopus 로고    scopus 로고
    • COOLCAT: An entropy-based algorithm for categorical clustering
    • Barbara, D.; Li, Y.; Couto, J. 2002. COOLCAT: An entropy-based algorithm for categorical clustering, In: CIKM Conf.; 582-589.
    • (2002) CIKM Conf. , pp. 582-589
    • Barbara, D.1    Li, Y.2    Couto, J.3
  • 6
    • 52949121468 scopus 로고    scopus 로고
    • On data labeling for clustering categorical data
    • H.L. Chen, K.T. Chuang, and M.S. Chen On data labeling for clustering categorical data IEEE Trans. Knowl. Data Eng. 20 11 2008 1458 1472
    • (2008) IEEE Trans. Knowl. Data Eng. , vol.20 , Issue.11 , pp. 1458-1472
    • Chen, H.L.1    Chuang, K.T.2    Chen, M.S.3
  • 9
    • 70449699648 scopus 로고    scopus 로고
    • Enhanced soft subspace clustering integrating within-cluster and between-cluster information
    • Z. Deng, K.S. Choi, F.L. Chung, and S. Wang Enhanced soft subspace clustering integrating within-cluster and between-cluster information Pattern Recognit. 43 2010 767 778
    • (2010) Pattern Recognit. , vol.43 , pp. 767-778
    • Deng, Z.1    Choi, K.S.2    Chung, F.L.3    Wang, S.4
  • 13
    • 0242387333 scopus 로고    scopus 로고
    • Mafia: Efficient and scalable subspace clustering for very large data sets
    • Northwestern University, 2145 Sheridan Road, Evanston IL 60208
    • Goil, S.; Nagesh, H.; Choudhary, A.; 1999. Mafia: Efficient and scalable subspace clustering for very large data sets. Technical Report CPDC-TR-9906-010, Northwestern University, 2145 Sheridan Road, Evanston IL 60208.
    • (1999) Technical Report CPDC-TR-9906-010
    • Goil, S.1    Nagesh, H.2    Choudhary, A.3
  • 14
    • 0001337675 scopus 로고
    • A new similarity index based on probability
    • D.W. Goodall A new similarity index based on probability Biometric 22 1966 882 907
    • (1966) Biometric , vol.22 , pp. 882-907
    • Goodall, D.W.1
  • 16
    • 0010486274 scopus 로고    scopus 로고
    • A fast clustering algorithm to cluster very large categorical data sets in data mining
    • Huang, Z. 1997. A fast clustering algorithm to cluster very large categorical data sets in data mining. In: Proc. SIGMOD DMKD Workshop, 1-8.
    • (1997) Proc. SIGMOD DMKD Workshop , pp. 1-8
    • Huang, Z.1
  • 19
    • 34347228671 scopus 로고    scopus 로고
    • An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data
    • DOI 10.1109/TKDE.2007.1048
    • L. Jing, M.K. Ng, and J.Z. Huang An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data IEEE Trans. Knowl. Data Eng. 19 8 2007 1026 1041 (Pubitemid 47000341)
    • (2007) IEEE Transactions on Knowledge and Data Engineering , vol.19 , Issue.8 , pp. 1026-1041
    • Jing, L.1    Ng, M.K.2    Huang, J.Z.3
  • 20
    • 0036649290 scopus 로고    scopus 로고
    • Unsupervised learning with mixed numeric and nominal data
    • C. Li, and G. Biswas Unsupervised learning with mixed numeric and nominal data IEEE Trans. Knowl. Data Eng. 14 4 2002 673 690
    • (2002) IEEE Trans. Knowl. Data Eng. , vol.14 , Issue.4 , pp. 673-690
    • Li, C.1    Biswas, G.2
  • 24
    • 33845981111 scopus 로고    scopus 로고
    • Clicks: An effective algorithm for mining subspace clusters in categorical datasets
    • DOI 10.1016/j.datak.2006.01.005, PII S0169023X06000176
    • M.J. Zaki, M. Peters, T. Assent, and T. Seidl CLICKS: An effective algorithm for mining subspace clusters in categorical datasets Data Knowl. Eng. 60 1 2007 51 70 (Pubitemid 46053589)
    • (2007) Data and Knowledge Engineering , vol.60 , Issue.1 , pp. 51-70
    • Zaki, M.J.1    Peters, M.2    Assent, I.3    Seidl, T.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.