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Volumn 21, Issue 5, 2009, Pages 652-665

Catching the trend: A framework for clustering concept-drifting categorical data

Author keywords

Categorical clustering; Data labeling; Data mining

Indexed keywords

INFORMATION MANAGEMENT; LABELING;

EID: 63449114954     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2008.192     Document Type: Article
Times cited : (41)

References (35)
  • 8
    • 36849005505 scopus 로고    scopus 로고
    • Evolutionary Spectral Clustering by Incorporating Temporal Smoothness
    • Y. Chi, X.-D. Song, D.-Y. Zhou, K. Hino, and B.L. Tseng, "Evolutionary Spectral Clustering by Incorporating Temporal Smoothness," Proc. ACM SIGKDD '07, pp. 153-162, 2007.
    • (2007) Proc. ACM SIGKDD '07 , pp. 153-162
    • Chi, Y.1    Song, X.-D.2    Zhou, D.-Y.3    Hino, K.4    Tseng, B.L.5
  • 11
    • 0343442766 scopus 로고
    • Knowledge Acquisition via Incremental Conceptual Clustering
    • D.H. Fisher, "Knowledge Acquisition via Incremental Conceptual Clustering," Machine Learning, 1987.
    • (1987) Machine Learning
    • Fisher, D.H.1
  • 12
    • 33845501194 scopus 로고    scopus 로고
    • Detection and Classification of Changes in Evolving Data Streams
    • M.M. Gaber and P.S. Yu, "Detection and Classification of Changes in Evolving Data Streams," Int'l J. Information Technology and Decision Making, vol. 5, no. 4, pp. 659-670, 2006.
    • (2006) Int'l J. Information Technology and Decision Making , vol.5 , Issue.4 , pp. 659-670
    • Gaber, M.M.1    Yu, P.S.2
  • 14
    • 0034133769 scopus 로고    scopus 로고
    • Clustering Categorical Data: An Approach Based on Dynamical Systems
    • D. Gibson, J.M. Kleinberg, and P. Raghavan, "Clustering Categorical Data: An Approach Based on Dynamical Systems," VLDB J., vol. 8, nos. 3-4, pp. 222-236, 2000.
    • (2000) VLDB J , vol.8 , Issue.3-4 , pp. 222-236
    • Gibson, D.1    Kleinberg, J.M.2    Raghavan, P.3
  • 19
    • 27144536001 scopus 로고    scopus 로고
    • Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
    • Z. Huang, "Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values," Data Mining and Knowledge Discovery, 1998.
    • (1998) Data Mining and Knowledge Discovery
    • Huang, Z.1
  • 20
    • 0032595161 scopus 로고    scopus 로고
    • Z. Huang and M.K. Ng, A Fuzzy k-Modes Algorithm for Clustering Categorical Data, ÍEEE Trans. Fuzzy Systems, 1999.
    • Z. Huang and M.K. Ng, "A Fuzzy k-Modes Algorithm for Clustering Categorical Data," ÍEEE Trans. Fuzzy Systems, 1999.
  • 25
    • 37549007987 scopus 로고    scopus 로고
    • A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites
    • Feb
    • O. Nasraoui, M. Soliman, E. Saka, A. Badia, and R. Germain, "A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites," IEEE Trans. Knowledge and Data Eng., vol. 20, no. 2, pp. 202-215, Feb. 2008.
    • (2008) IEEE Trans. Knowledge and Data Eng , vol.20 , Issue.2 , pp. 202-215
    • Nasraoui, O.1    Soliman, M.2    Saka, E.3    Badia, A.4    Germain, R.5
  • 27
    • 0016572913 scopus 로고
    • A Vector Space Model for Automatic Indexing
    • G. Salton, A. Wong, and C.S. Yang, "A Vector Space Model for Automatic Indexing," Comm. ACM, vol. 18, no. 11, pp. 613-620, 1975.
    • (1975) Comm. ACM , vol.18 , Issue.11 , pp. 613-620
    • Salton, G.1    Wong, A.2    Yang, C.S.3
  • 29
    • 0036567251 scopus 로고    scopus 로고
    • An Iterative Initial-Points Refinement Algorithm for Categorical Data Clustering
    • Y. Sun, Q. Zhu, and Z. Chen, "An Iterative Initial-Points Refinement Algorithm for Categorical Data Clustering," Pattern Recognition Letters, vol. 23, no. 7, 2002.
    • (2002) Pattern Recognition Letters , vol.23 , Issue.7
    • Sun, Y.1    Zhu, Q.2    Chen, Z.3
  • 30
    • 77952415079 scopus 로고    scopus 로고
    • Mining Concept-Drifting Data Streams Using Ensemble Classifiers
    • H. Wang, W. Fan, P. Yun, and J. Han, "Mining Concept-Drifting Data Streams Using Ensemble Classifiers," Proc. ACM SIGKDD, 2003.
    • (2003) Proc. ACM SIGKDD
    • Wang, H.1    Fan, W.2    Yun, P.3    Han, J.4
  • 31
    • 0030126609 scopus 로고    scopus 로고
    • Learning in the Presence of Concept Drift and Hidden Contexts
    • G. Widmer and M. Kubat, "Learning in the Presence of Concept Drift and Hidden Contexts," Machine Learning, 1996.
    • (1996) Machine Learning
    • Widmer, G.1    Kubat, M.2
  • 32
    • 34648854456 scopus 로고    scopus 로고
    • Clustering over Multiple Evolving Streams by Events and Correlations
    • Oct
    • M.-Y. Yeh, B.-R. Dai, and M.-S. Chen, "Clustering over Multiple Evolving Streams by Events and Correlations," IEEE Trans. Knowledge and Data Eng., vol. 19, no. 10, pp. 1349-1362, Oct. 2007.
    • (2007) IEEE Trans. Knowledge and Data Eng , vol.19 , Issue.10 , pp. 1349-1362
    • Yeh, M.-Y.1    Dai, B.-R.2    Chen, M.-S.3
  • 33
    • 28444449780 scopus 로고    scopus 로고
    • Clicks: Mining Subspace Clusters in Categorical Data via k-Partite Maximal Cliques
    • M.J. Zaki and M. Peters, "Clicks: Mining Subspace Clusters in Categorical Data via k-Partite Maximal Cliques," Proc. 21st Int'l Conf. Data Eng., 2005.
    • (2005) Proc. 21st Int'l Conf. Data Eng
    • Zaki, M.J.1    Peters, M.2
  • 34
    • 0030157145 scopus 로고    scopus 로고
    • BIRCH: An Efficient Data Clustering Method for Very Large Database
    • T. Zhang, R. Ramakrishnan, and M. Livny, "BIRCH: An Efficient Data Clustering Method for Very Large Database," Proc. ACM SIGMOD, 1996.
    • (1996) Proc. ACM SIGMOD
    • Zhang, T.1    Ramakrishnan, R.2    Livny, M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.