메뉴 건너뛰기




Volumn 46, Issue 1, 2013, Pages

Data stream clustering: A survey

Author keywords

Data stream clustering; Online clustering

Indexed keywords

DATA STREAM CLUSTERING; DATA STREAM MINING; EXPERIMENTAL METHODOLOGY; INCREMENTAL PROCESSING; NETWORK INTRUSION DETECTION; ONLINE-CLUSTERING; STATE-OF-THE-ART ALGORITHMS; STOCK MARKET ANALYSIS;

EID: 84887425296     PISSN: 03600300     EISSN: 15577341     Source Type: Journal    
DOI: 10.1145/2522968.2522981     Document Type: Article
Times cited : (468)

References (110)
  • 2
    • 4243114094 scopus 로고    scopus 로고
    • Approximating extent measures of points
    • AGARWAL, P. K., HAR-PELED, S., AND VARADARAJAN, K. R. 2004a. Approximating extent measures of points. J. ACM 51, 4, 606-635.
    • (2004) J ACM , vol.51 , Issue.4 , pp. 606-635
    • Agarwal, P.K.1    Har-Peled, S.2    Varadarajan, K.R.3
  • 5
    • 84855559607 scopus 로고    scopus 로고
    • A segment-based framework for modeling and mining data streams
    • AGGARWAL, C. C. 2010. A segment-based framework for modeling and mining data streams. Knowl. Inf. Syst, 30, 1, 1-29.
    • (2010) Knowl. Inf. Syst , vol.30 , Issue.1 , pp. 1-29
    • Aggarwal, C.C.1
  • 10
    • 84856921698 scopus 로고    scopus 로고
    • On clustering large number of data streams
    • AGHBARI, Z. A., KAMEL, I., AND AWAD, T. 2012. On clustering large number of data streams. Intell. Data Anal. 16, 1, 69-91.
    • (2012) Intell. Data Anal , vol.16 , Issue.1 , pp. 69-91
    • Aghbari, Z.A.1    Kamel, I.2    Awad, T.3
  • 19
    • 0038205905 scopus 로고    scopus 로고
    • Requirements for clustering data streams
    • BARBARA, D. 2002. Requirements for clustering data streams. SIGKDD Explorations 3, 23-27.
    • (2002) SIGKDD Explorations , vol.3 , pp. 23-27
    • Barbara, D.1
  • 20
    • 0016557674 scopus 로고
    • Multidimensional binary search trees used for associative searching
    • BENTLEY, J. L. 1975. Multidimensional binary search trees used for associative searching. Comm. ACM 18, 9, 509-517.
    • (1975) Comm ACM , vol.18 , Issue.9 , pp. 509-517
    • Bentley, J.L.1
  • 21
    • 0000108833 scopus 로고
    • Decomposable searching problems I: Static-to-dynamic transformation
    • BENTLEY, J. L. AND SAXE, J. B. 1980. Decomposable searching problems I: Static-to-dynamic transformation. J. Algor. 1, 4, 301-358.
    • (1980) J. Algor , vol.1 , Issue.4 , pp. 301-358
    • Bentley, J.L.1    Saxe, J.B.2
  • 34
    • 0002815587 scopus 로고    scopus 로고
    • A general method for scaling up machine learning algorithms and its application to clustering
    • Morgan Kaufmann Publishers, San Francisco
    • DOMINGOS, P. AND HULTEN, G. 2001. A general method for scaling up machine learning algorithms and its application to clustering. In Proceedings of the 8th International Conference on Machine Learning. Morgan Kaufmann Publishers, San Francisco, 106-113.
    • (2001) Proceedings of the 8th International Conference on Machine Learning , pp. 106-113
    • Domingos, P.1    Hulten, G.2
  • 38
    • 0002679222 scopus 로고    scopus 로고
    • Scalability for clustering algorithms revisited
    • FARNSTROM, F., LEWIS, J., AND ELKAN, C. 2000. Scalability for clustering algorithms revisited. SIGKDD Exploration Newslett. 2, 1, 51-57.
    • (2000) SIGKDD Exploration Newslett , vol.2 , Issue.1 , pp. 51-57
    • Farnstrom, F.1    Lewis, J.2    Elkan, C.3
  • 39
    • 0002433547 scopus 로고    scopus 로고
    • From data mining to knowledge discovery: An overview
    • American Association for Artificial Intelligence, Menlo Park, CA
    • FAYYAD, U., PIATETSKY-SHAPIRO, G., AND SMYTH, P. 1996. From data mining to knowledge discovery: An overview. In Advances in Knowledge Discovery and Data Mining. American Association for Artificial Intelligence, Menlo Park, CA, 1-34.
    • (1996) Advances in Knowledge Discovery and Data Mining , pp. 1-34
    • Fayyad, U.1    Piatetsky-Shapiro, G.2    Smyth, P.3
  • 45
    • 79551527268 scopus 로고    scopus 로고
    • Clustering distributed sensor data streams using local processing and reduced communication
    • GAMA, J., PEREIRA, P. R., AND LOPES, L. 2011. Clustering distributed sensor data streams using local processing and reduced communication. Intell. Data Anal. 15, 1, 3-28.
    • (2011) Intell. Data Anal , vol.15 , Issue.1 , pp. 3-28
    • Gama, J.1    Pereira, P.R.2    Lopes, L.3
  • 49
    • 0021938963 scopus 로고
    • Clustering to minimize the maximum intercluster distance
    • GONZALEZ, T. F. 1985. Clustering to minimize the maximum intercluster distance. The or. Comput. Sci. 38, 293-306.
    • (1985) The Or. Comput. Sci , vol.38 , pp. 293-306
    • Gonzalez, T.F.1
  • 53
    • 77955150621 scopus 로고    scopus 로고
    • REMM: Extensible markov model for data stream clustering in R
    • HAHSLER, M. AND DUNHAM, M. H. 2010. rEMM: Extensible markov model for data stream clustering in r. J. Statist. Softw. 35, 5, 1-31.
    • (2010) J. Statist. Softw , vol.35 , Issue.5 , pp. 1-31
    • Hahsler, M.1    Dunham, M.H.2
  • 54
    • 84880099592 scopus 로고    scopus 로고
    • Temporal structure learning for clustering massive data streams in real-time
    • SIAM/Omnipress
    • HAHSLER, M. AND DUNHAM, M. H. 2011. Temporal structure learning for clustering massive data streams in real-time. In Proceedings of the SIAM Conference on Data Mining. SIAM/Omnipress, 664-675.
    • (2011) Proceedings of the SIAM Conference on Data Mining , pp. 664-675
    • Hahsler, M.1    Dunham, M.H.2
  • 59
    • 77950369345 scopus 로고    scopus 로고
    • Data clustering: 50 years beyond k-means
    • JAIN, A. K. 2009. Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31, 651-666.
    • (2009) Pattern Recogn. Lett , vol.31 , pp. 651-666
    • Jain, A.K.1
  • 60
    • 33644920942 scopus 로고    scopus 로고
    • Research issues in data stream association rule mining
    • JIANG, N. AND GRUENWALD, L. 2006. Research issues in data stream association rule mining. SIGMOD Rec. 35, 1, 14-19.
    • (2006) SIGMOD Rec , vol.35 , Issue.1 , pp. 14-19
    • Jiang, N.1    Gruenwald, L.2
  • 62
    • 79958694733 scopus 로고    scopus 로고
    • Clustering time series data stream-A literature survey
    • KAVITHA, V. AND PUNITHAVALLI, M. 2010. Clustering time series data stream-A literature survey. Int. J. Comput. Sci. Inf. Secur. 8, 1, 289-294.
    • (2010) Int. J. Comput. Sci. Inf. Secur , vol.8 , Issue.1 , pp. 289-294
    • Kavitha, V.1    Punithavalli, M.2
  • 66
    • 80053927938 scopus 로고    scopus 로고
    • The clustree: Indexing microclusters for anytime stream mining
    • KRANEN, P.,ASSENT, I.,BALDAUF, C., AND SEIDL, T. 2011. The clustree: Indexing microclusters for anytime stream mining. Knowl. Inf. Syst. 29, 2, 249-272.
    • (2011) Knowl. Inf. Syst , vol.29 , Issue.2 , pp. 249-272
    • Kranen, P.1    Assent, I.2    Baldauf, C.3    Seidl, T.4
  • 69
    • 79960459092 scopus 로고    scopus 로고
    • Data stream clustering algorithm based on affinity propagation and density
    • LI, Y. AND TAN, B. H. 2011. Data stream clustering algorithm based on affinity propagation and density. Advanced Materials Res. 267, 444-449.
    • (2011) Advanced Materials Res , vol.267 , pp. 444-449
    • Li, Y.1    Tan, B.H.2
  • 71
    • 0020102027 scopus 로고
    • Least squares quantization in pcm
    • LLOYD, S. P. 1982. Least squares quantization in pcm. IEEE Trans. Inf. The ory 28, 2, 129-137.
    • (1982) IEEE Trans. Inf. The Ory , vol.28 , Issue.2 , pp. 129-137
    • Lloyd, S.P.1
  • 72
    • 56249119506 scopus 로고    scopus 로고
    • Incremental clustering of dynamic data streams using connectivity based representative points
    • LUHR, S. AND LAZARESCU, M. 2009. Incremental clustering of dynamic data streams using connectivity based representative points. Data Knowl. Engin. 68, 1-27.
    • (2009) Data Knowl. Engin , vol.68 , pp. 1-27
    • Luhr, S.1    Lazarescu, M.2
  • 75
  • 76
    • 2942516558 scopus 로고
    • Extension of the limit the orems of probability the ory to a sum of variables connected in a chain
    • R. Howard, Ed., JohnWiley and Sons, Chapter Appendix B
    • MARKOV, A. 1971. Extension of the limit the orems of probability the ory to a sum of variables connected in a chain. In Dynamic Probabilistic Systems, Vol. 1, R. Howard, Ed., JohnWiley and Sons, Chapter Appendix B, 552-577.
    • (1971) Dynamic Probabilistic Systems , vol.1 , pp. 552-577
    • Markov, A.1
  • 80
    • 84887439851 scopus 로고    scopus 로고
    • Ungar, M. Craven, D. Gunopulos, and T. Eliassi-Rad, Eds., ACM Press, New York, 935-940
    • Ungar, M. Craven, D. Gunopulos, and T. Eliassi-Rad, Eds., ACM Press, New York, 935-940.
  • 84
    • 84856853817 scopus 로고    scopus 로고
    • A framework to monitor clusters evolution applied to economy and finance problems
    • OLIVEIRA, M. D. B. AND GAMA, J. 2012. A framework to monitor clusters evolution applied to economy and finance problems. Intell. Data Anal. 16, 1, 93-111.
    • (2012) Intell. Data Anal , vol.16 , Issue.1 , pp. 93-111
    • Oliveira, M.D.B.1    Gama, J.2
  • 85
    • 54249118625 scopus 로고    scopus 로고
    • The battle of the water sensor networks (BWSN): A design challenge for engineers and algorithms
    • OSTFELD, A.,UBER, J. G., SALOMONS, E., ET AL. 2008. The battle of the water sensor networks (BWSN): A design challenge for engineers and algorithms. J. Water Resources Plan. Manag. 134, 556.
    • (2008) J. Water Resources Plan. Manag , vol.134 , pp. 556
    • Ostfeld, A.1    Uber, J.G.2    Salomons, E.3    Al, E.T.4
  • 86
    • 34447276480 scopus 로고    scopus 로고
    • Cell trees: An adaptive synopsis structure for clustering multidimensional on-line data streams
    • PARK, N. H. AND SUK LEE,W. 2007. Cell trees: An adaptive synopsis structure for clustering multidimensional on-line data streams. Data Knowl. Engin. 63, 2, 528-549.
    • (2007) Data Knowl. Engin , vol.63 , Issue.2 , pp. 528-549
    • Park, N.H.1    Suk Lee, W.2
  • 90
    • 84860214132 scopus 로고    scopus 로고
    • Evidential evolving gustafson-kessel algorithm for online data streams partitioning using belief function the ory
    • SERIR, L.,RAMASSO, E., AND ZERHOUNI, N. 2012. Evidential evolving gustafson-kessel algorithm for online data streams partitioning using belief function the ory. Int. J. Approximate Reason. 53, 5, 1-22.
    • (2012) Int. J. Approximate Reason , vol.53 , Issue.5 , pp. 1-22
    • Serir, L.1    Ramasso, E.2    Zerhouni, N.3
  • 92
    • 84862701443 scopus 로고    scopus 로고
    • A clustering approach for sampling data streams in sensor networks
    • SILVA, A.,CHIKY, R., ANDHEBRAIL,G. 2011. A clustering approach for sampling data streams in sensor networks. Knowl. Inf. Syst. 32, 1, 1-23.
    • (2011) Knowl. Inf. Syst. , vol.32 , Issue.1 , pp. 1-23
    • Silva, A.1    Chiky, R.2    Hebrail, G.3
  • 95
    • 33747479974 scopus 로고
    • Sur la division des corp materiels en parties
    • STEINHAUS, H. 1956. Sur la division des corp materiels en parties. Bull. Acad. Polon. Sci 1, 801-804.
    • (1956) Bull. Acad. Polon. Sci , vol.1 , pp. 801-804
    • Steinhaus, H.1
  • 104
    • 21944442892 scopus 로고    scopus 로고
    • BIRCH: A new data clustering algorithm and its applications
    • ZHANG, T.,RAMAKRISHNAN, R., AND LIVNY,M. 1997. BIRCH: A new data clustering algorithm and its applications. Data Mining Knowl. Discov. 1, 2, 141-182.
    • (1997) Data Mining Knowl. Discov , vol.1 , Issue.2 , pp. 141-182
    • Zhang, T.1    Ramakrishnan, R.2    Livny, M.3
  • 106
    • 84887440209 scopus 로고    scopus 로고
    • Self-adaptive change detection in streaming data with nonstationary distribution
    • Springer
    • ZHANG, X. AND WANG, W. 2010. Self-adaptive change detection in streaming data with nonstationary distribution. In Advanced Data Mining and Applications. Springer, 1-12.
    • (2010) Advanced Data Mining and Applications , pp. 1-12
    • Zhang, X.1    Wang, W.2
  • 108
    • 43249088014 scopus 로고    scopus 로고
    • Tracking clusters in evolving data streams over sliding windows
    • ZHOU, A., CAO, F.,QIAN, W., AND JIN, C. 2008. Tracking clusters in evolving data streams over sliding windows. Knowl. Inf. Syst. 15, 2, 181-214.
    • (2008) Knowl. Inf. Syst , vol.15 , Issue.2 , pp. 181-214
    • Zhou, A.1    Cao, F.2    Qian, W.3    Jin, C.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.