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Volumn 3518 LNAI, Issue , 2005, Pages 639-649

Cl-GBI: A novel approach for extracting typical patterns from graph-structured data

Author keywords

[No Author keywords available]

Indexed keywords

CURRICULA; DATA MINING; EDUCATION; FEATURE EXTRACTION; INFORMATION TECHNOLOGY; LEARNING SYSTEMS; TEACHING;

EID: 26944438666     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11430919_74     Document Type: Conference Paper
Times cited : (8)

References (16)
  • 2
    • 2442483205 scopus 로고    scopus 로고
    • Mining molecular fragments: Finding relevant substructures of molecules
    • Borgelt, C. and Berthold, M.R. 2002. Mining Molecular Fragments: Finding Relevant Substructures of Molecules. In: Proc. ICDM 2002, pp.51-58.
    • (2002) Proc. ICDM 2002 , pp. 51-58
    • Borgelt, C.1    Berthold, M.R.2
  • 4
    • 0027652468 scopus 로고
    • Substructure discovery using minimum description length and background knowledge
    • Cook, D. J. and Holder, L. B. 1994. Substructure Discovery Using Minimum Description Length and Background Knowledge, Artificial Intelligence Research, Vol. 1, pp. 231-255.
    • (1994) Artificial Intelligence Research , vol.1 , pp. 231-255
    • Cook, D.J.1    Holder, L.B.2
  • 5
    • 0008690083 scopus 로고    scopus 로고
    • The graph isomorphism problem
    • Department of Computer Science, University of Alberta, Edmonton, Canada
    • Fortin, S. 1996. The Graph Isomorphism Problem, Technical Report TR96-20, Department of Computer Science, University of Alberta, Edmonton, Canada.
    • (1996) Technical Report , vol.TR96-20
    • Fortin, S.1
  • 6
    • 7444223549 scopus 로고    scopus 로고
    • Classifier construction by graph-based induction for graph-structured data
    • Gaemsakul, W., Matsuda, T., Yoshida, T., Motoda, M., and Washio, T. 2003. Classifier Construction by Graph-Based Induction for Graph-Structured Data, In: Proc. PAKDD 2003, pp. 52-62.
    • (2003) Proc. PAKDD 2003 , pp. 52-62
    • Gaemsakul, W.1    Matsuda, T.2    Yoshida, T.3    Motoda, M.4    Washio, T.5
  • 7
    • 78149328300 scopus 로고    scopus 로고
    • Efficient mining of frequent subgraphs in the presence of isomorphism
    • Huan, J., Wang, W., and Prins, J., 2003. Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism, In: Proc. ICDM 2003, pp. 549-552.
    • (2003) Proc. ICDM 2003 , pp. 549-552
    • Huan, J.1    Wang, W.2    Prins, J.3
  • 8
    • 0037364958 scopus 로고    scopus 로고
    • Complete mining of frequent patterns from graphs: Mining graph data
    • Inokuchi, A., Washio, T., and Motoda, H. 2003. Complete Mining of Frequent Patterns from Graphs: Mining Graph Data, Machine Learning, Vol. 50, No. 3, pp. 321-354.
    • (2003) Machine Learning , vol.50 , Issue.3 , pp. 321-354
    • Inokuchi, A.1    Washio, T.2    Motoda, H.3
  • 9
    • 4544296752 scopus 로고    scopus 로고
    • A fast algorithm for mining frequent connected subgraphs
    • Tokyo Research Laboratory, IBM Japan
    • Inokuchi, A., Washio, T., Nishimura, K., and Motoda, H. 2002. A Fast Algorithm for Mining Frequent Connected Subgraphs, IBM Research Report RT0448, Tokyo Research Laboratory, IBM Japan.
    • (2002) IBM Research Report , vol.RT0448
    • Inokuchi, A.1    Washio, T.2    Nishimura, K.3    Motoda, H.4
  • 10
    • 4544385908 scopus 로고    scopus 로고
    • An efficient algorithm for discovering frequent subgraphs
    • Kuramochi, M. and Karypis, G. 2004. An Efficient Algorithm for Discovering Frequent Subgraphs, IEEE Trans. Knowledge and Data Engineering, Vol. 16, No. 9, pp. 1038-1051.
    • (2004) IEEE Trans. Knowledge and Data Engineering , vol.16 , Issue.9 , pp. 1038-1051
    • Kuramochi, M.1    Karypis, G.2
  • 11
    • 19544365404 scopus 로고    scopus 로고
    • GREW-A scalable frequent subgraph discovery algorithm
    • Kuramochi, M. and Karypis, G. 2004. GREW-A Scalable Frequent Subgraph Discovery Algorithm, In: Proc. ICDM 2004, pp. 439-442.
    • (2004) Proc. ICDM 2004 , pp. 439-442
    • Kuramochi, M.1    Karypis, G.2
  • 12
    • 84949762623 scopus 로고    scopus 로고
    • Mining patterns from structured data by beam-wise graph-based induction
    • Matsuda, T., Motoda, H., Yoshida, T., and Washio, T. 2002. Mining Patterns from Structured Data by Beam-wise Graph-Based Induction, In: Proc. DS 2002, pp. 422-429.
    • (2002) Proc. DS 2002 , pp. 422-429
    • Matsuda, T.1    Motoda, H.2    Yoshida, T.3    Washio, T.4
  • 13
    • 33744584654 scopus 로고
    • Induction of decision trees
    • Quinlan, J.R. 1986. Induction of Decision Trees, Machine Learning, Vol. 1, pp. 81-106.
    • (1986) Machine Learning , vol.1 , pp. 81-106
    • Quinlan, J.R.1
  • 15
    • 78149333073 scopus 로고    scopus 로고
    • GSpan: Graph-based structure pattern mining
    • Yan, X. and Han, J. 2002. gSpan: Graph-Based Structure Pattern Mining, In: Proc. ICDM 2002, pp. 721-724.
    • (2002) Proc. ICDM 2002 , pp. 721-724
    • Yan, X.1    Han, J.2
  • 16
    • 0029306031 scopus 로고
    • CLIP: Concept learning from inference patterns
    • Yoshida, K. and Motoda, M. 1995. CLIP: Concept Learning from Inference Patterns, Artificial Intelligence, Vol. 75, No. 1, pp. 63-92.
    • (1995) Artificial Intelligence , vol.75 , Issue.1 , pp. 63-92
    • Yoshida, K.1    Motoda, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.