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Volumn 3, Issue 1, 2010, Pages 975-984

On dense pattern mining in graph streams

Author keywords

[No Author keywords available]

Indexed keywords

DENSE PATTERNS; DENSE STRUCTURES; PROBABILISTIC ALGORITHM; PROBABILISTIC APPROACHES; STREAM PATTERNS; STRUCTURAL PATTERN; SYNTHETIC DATASETS; UNDERLYING GRAPHS;

EID: 80052677277     PISSN: None     EISSN: 21508097     Source Type: Conference Proceeding    
DOI: 10.14778/1920841.1920964     Document Type: Article
Times cited : (59)

References (13)
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    • Managing and Mining Graph Data
    • C. Aggarwal, and H. Wang. Managing and Mining Graph Data. Springer, 2010.
    • (2010) Springer
    • Aggarwal, C.1    Wang, H.2
  • 3
    • 19544377965 scopus 로고    scopus 로고
    • Moment: Maintaining closed frequent itemsets over a stream sliding window
    • Y. Chi, H. Wang, P.S. Yu, and R. R. Muntz. Moment: Maintaining closed frequent itemsets over a stream sliding window. ICDM Conference, 2004.
    • (2004) ICDM Conference
    • Chi, Y.1    Wang, H.2    Yu, P.S.3    Muntz, R.R.4
  • 6
    • 49749125569 scopus 로고    scopus 로고
    • Efficient Algorithms for mining significant substructures from graphs with quality guarantees
    • H. He, and A. K. Singh. Efficient Algorithms for mining significant substructures from graphs with quality guarantees. ICDM Conference, 2007.
    • (2007) ICDM Conference
    • He, H.1    Singh, A.K.2
  • 7
    • 34548544986 scopus 로고    scopus 로고
    • An algorithm for in-core frequent itemset mining on streaming data
    • R. Jin, and G. Agrawal. An algorithm for in-core frequent itemset mining on streaming data. ICDM Conference, 2005.
    • (2005) ICDM Conference
    • Jin, R.1    Agrawal, G.2
  • 9
    • 32344444324 scopus 로고    scopus 로고
    • On mining cross-graph quasi-cliques
    • J. Pei, D. Jiang, and A. Zhang. On mining cross-graph quasi-cliques. KDD, 2005.
    • (2005) KDD
    • Pei, J.1    Jiang, D.2    Zhang, A.3
  • 10
    • 67649644367 scopus 로고    scopus 로고
    • GraphSig: A scalable approach to min- ing significant subgraphs in large graph databases
    • S. Ranu, and A. K. Singh. GraphSig: A scalable approach to min- ing significant subgraphs in large graph databases. ICDE Conference, 2009.
    • (2009) ICDE Conference
    • Ranu, S.1    Singh, A.K.2
  • 11
    • 77952334885 scopus 로고    scopus 로고
    • CloseGraph: Mining Closed Frequent Graph Patterns
    • X. Yan, and J. Han. CloseGraph: Mining Closed Frequent Graph Patterns. KDD, 2003.
    • (2003) KDD
    • Yan, X.1    Han, J.2
  • 12
    • 79953236929 scopus 로고    scopus 로고
    • False positive or false negative: Mining frequent itemsets from high speed transactional data streams
    • J. Xu Yu, Z. Chong, H. Lu, and A. Zhou. False positive or false negative: Mining frequent itemsets from high speed transactional data streams. VLDB Conference, 2004.
    • (2004) VLDB Conference
    • Xu Yu, J.1    Chong, Z.2    Lu, H.3    Zhou, A.4
  • 13
    • 84859240606 scopus 로고    scopus 로고
    • Out-of-core Coherent Closed Quasi-Clique Mining from Large Dense Graph Databases
    • Z. Zeng, J. Wang, L. Zhou, and G. Karypis. Out-of-core Coherent Closed Quasi-Clique Mining from Large Dense Graph Databases. ACM TODS, Vol 31(2), 2007.
    • (2007) ACM TODS , vol.31 , Issue.2
    • Zeng, Z.1    Wang, J.2    Zhou, L.3    Karypis, G.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.