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Volumn , Issue , 2003, Pages 432-436

A new distributed data mining model based on similarity

Author keywords

Distributed Data Mining(DDM); SBDDM; Similarity

Indexed keywords

COMPUTER SCIENCE; DATABASE SYSTEMS; DISTRIBUTED COMPUTER SYSTEMS; LEARNING ALGORITHMS;

EID: 0037928941     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/952613.952618     Document Type: Conference Paper
Times cited : (22)

References (23)
  • 2
    • 0027621699 scopus 로고
    • Mining associations between sets of items in massive databases
    • Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining associations between sets of items in massive databases. ACM-SIGMOD-1993 (pp. 207-216).
    • (1993) ACM-SIGMOD-1993 , pp. 207-216
    • Agrawal, R.1    Imielinski, T.2    Swami, A.3
  • 3
    • 0035007850 scopus 로고    scopus 로고
    • MAFIA: A maximal frequent itemset algorithm for transactional databases
    • Burdick, D., Calimlim, M., & Gehrke, J. (2001). MAFIA: A maximal frequent itemset algorithm for transactional databases. ICDE (pp. 443-452).
    • (2001) ICDE , pp. 443-452
    • Burdick, D.1    Calimlim, M.2    Gehrke, J.3
  • 6
    • 0037909298 scopus 로고    scopus 로고
    • Towards real time discovery from distributed information sources
    • Cho, V., & Wuthrich, B. (1998). Towards real time discovery from distributed information sources. PAKDD.
    • (1998) PAKDD
    • Cho, V.1    Wuthrich, B.2
  • 8
    • 84974698302 scopus 로고    scopus 로고
    • Context-based similarity methods for categorical attributes
    • Das, G., & Mannila, H. (2000). Context-based similarity methods for categorical attributes. PKDD (pp. 201-211).
    • (2000) PKDD , pp. 201-211
    • Das, G.1    Mannila, H.2
  • 10
    • 78149351437 scopus 로고    scopus 로고
    • Efficiently mining maximal frequent itemsets
    • Gouda, K., & Zaki, M. J. (2001). Efficiently mining maximal frequent itemsets. ICDM.
    • (2001) ICDM
    • Gouda, K.1    Zaki, M.J.2
  • 12
    • 0001282382 scopus 로고    scopus 로고
    • Collective data mining: A new perspective toward distributed data mining
    • H. Kargupta and P. Chan (Eds.). AAAI/MIT
    • Kargupta, H., Park, B., Hershbereger, D., & Johnson, E. (2000). Collective data mining: A new perspective toward distributed data mining. In H. Kargupta and P. Chan (Eds.), Advances in distributed data mining, 133-184. AAAI/MIT.
    • (2000) Advances in Distributed Data Mining , pp. 133-184
    • Kargupta, H.1    Park, B.2    Hershbereger, D.3    Johnson, E.4
  • 13
    • 0030646558 scopus 로고    scopus 로고
    • Distributed data mining of probabilistic knowledge
    • Lam, W., & Segre, A. M. (1997). Distributed data mining of probabilistic knowledge. ICDCS.
    • (1997) ICDCS
    • Lam, W.1    Segre, A.M.2
  • 14
    • 0038246856 scopus 로고    scopus 로고
    • Similarity testing between heterogeneous basket databases
    • Computer Science, Univ. of Rochester
    • Li, T., Ogihara, M., & Zhu, S. (2002). Similarity testing between heterogeneous basket databases (Technical Report 781). Computer Science, Univ. of Rochester.
    • (2002) Technical Report , vol.781
    • Li, T.1    Ogihara, M.2    Zhu, S.3
  • 15
    • 0037909286 scopus 로고    scopus 로고
    • Clustering distributed homogeneous datasets
    • Parthasarathy, S., & Ogihara, M. (2000). Clustering distributed homogeneous datasets. PKDD.
    • (2000) PKDD
    • Parthasarathy, S.1    Ogihara, M.2
  • 18
    • 0010362181 scopus 로고    scopus 로고
    • Ph.lic. thesis, University of Helsinki. Available as Report C-1998-42, University of Helsinki, Department of Computer Science, October 1998
    • Ronkainen, R. (1998). Attribute similarity and event sequence similarity in data mining. Ph.lic. thesis, University of Helsinki. Available as Report C-1998-42, University of Helsinki, Department of Computer Science, October 1998.
    • (1998) Attribute Similarity and Event Sequence Similarity in Data Mining
    • Ronkainen, R.1
  • 19
    • 0038246813 scopus 로고    scopus 로고
    • Defining diff as a data mining primitive
    • Subramonian, R. (1998). Defining diff as a data mining primitive. KDD.
    • (1998) KDD
    • Subramonian, R.1
  • 20
    • 0037909280 scopus 로고    scopus 로고
    • A framework for finding distributed data mining strategies that are intermediate between centralized strategies and in-place strategies
    • Turinsky, A., & Grossman, R. (2000). A framework for finding distributed data mining strategies that are intermediate between centralized strategies and in-place strategies. Proc. of KDD Workshop on Distributed Data Mining.
    • (2000) Proc. of KDD Workshop on Distributed Data Mining
    • Turinsky, A.1    Grossman, R.2
  • 21
    • 0030703799 scopus 로고    scopus 로고
    • Distributed cooperative bayesian learning strategies
    • New York: ACM
    • Yamanishi, K. (1997). Distributed cooperative bayesian learning strategies. Proceedings of COLT 97 (pp. 250-262). New York: ACM.
    • (1997) Proceedings of COLT 97 , pp. 250-262
    • Yamanishi, K.1
  • 23
    • 0037909281 scopus 로고    scopus 로고
    • CoFD: An algorithm for non-distance based clustering in high dimensional spaces
    • Zhu, S., Li, T., & Ogihara, M. (2002). CoFD: An algorithm for non-distance based clustering in high dimensional spaces. DaWaK.
    • (2002) DaWaK
    • Zhu, S.1    Li, T.2    Ogihara, M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.