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Volumn 23, Issue 2, 2010, Pages 199-224

POTMiner: Mining ordered, unordered, and partially-ordered trees

Author keywords

Data mining; Frequent patterns; Induced and embedded subtrees; Partially ordered trees

Indexed keywords

FORESTRY; TREES (MATHEMATICS);

EID: 77952091991     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-009-0213-3     Document Type: Article
Times cited : (10)

References (37)
  • 2
    • 0141764033 scopus 로고    scopus 로고
    • A tree projection algorithm for generation of frequent item sets
    • Agarwal RC et al (2001) A tree projection algorithm for generation of frequent item sets. J Parallel Distrib Comput 61(3): 350-371.
    • (2001) J Parallel Distrib Comput , vol.61 , Issue.3 , pp. 350-371
    • Agarwal, R.C.1
  • 5
    • 0242360778 scopus 로고    scopus 로고
    • Discovering frequent substructures in large unordered trees
    • Springer, Berlin
    • Asai T et al (2003) Discovering frequent substructures in large unordered trees. In: Discovery science. Lecture Notes in Artificial Intelligence, vol 2843. Springer, Berlin, pp 47-61.
    • (2003) Discovery Science. Lecture Notes in Artificial Intelligence , vol.2843 , pp. 47-61
    • Asai, T.1
  • 6
    • 38449083474 scopus 로고    scopus 로고
    • Hierarchical program representation for program element matching
    • Berzal F et al (2007) Hierarchical program representation for program element matching. In: IDEAL'07. Lecture Notes in Computer Science, vol 4881, pp 467-476.
    • (2007) IDEAL'07. Lecture Notes in Computer Science , vol.4881 , pp. 467-476
    • Berzal, F.1
  • 7
    • 33745180487 scopus 로고    scopus 로고
    • To see the wood for the trees: Mining frequent tree patterns
    • 11-13 March 2004, Hinterzarten,Germany. Revised Selected Papers. Lecture Notes in Computer Science, Springer, Berlin
    • Bringmann B (2006) To see the wood for the trees: mining frequent tree patterns. In: Constraint-based mining and inductive databases, European workshop on inductive databases and constraint based mining. 11-13 March 2004, Hinterzarten, Germany. Revised Selected Papers. Lecture Notes in Computer Science, vol 3848. Springer, Berlin, pp 38-63.
    • (2006) Constraint-based Mining and Inductive Databases, European Workshop on Inductive Databases and Constraint Based Mining. , vol.3848 , pp. 38-63
    • Bringmann, B.1
  • 8
    • 0030379749 scopus 로고    scopus 로고
    • Efficient mining of association rules in distributed databases
    • Cheung DW-L et al (1996) Efficient mining of association rules in distributed databases. IEEE Trans Knowl Data Eng 8(6)911-922.
    • (1996) IEEE Trans Knowl Data Eng , vol.8 , Issue.6 , pp. 911-922
    • Cheung, D.W.-L.1
  • 9
    • 24044517207 scopus 로고    scopus 로고
    • Frequent subtree mining-an overview
    • Chi Y et al (2005a) Frequent subtree mining-an overview. Fundam Inform 66(1-2): 161-198.
    • (2005) Fundam Inform , vol.66 , Issue.1-2 , pp. 161-198
    • Chi, Y.1
  • 10
    • 14644411753 scopus 로고    scopus 로고
    • Mining closed and maximal frequent subtrees from databases of labeled rooted trees
    • Chi Y et al (2005b) Mining closed and maximal frequent subtrees from databases of labeled rooted trees. IEEE Trans Knowl Data Eng 17(2): 190-202.
    • (2005) IEEE Trans Knowl Data Eng , vol.17 , Issue.2 , pp. 190-202
    • Chi, Y.1
  • 11
    • 5444259929 scopus 로고    scopus 로고
    • HybridTreeMiner: An efficient algorithm for mining frequent rooted trees and free trees using canonical form
    • Chi Y et al (2004) HybridTreeMiner: an efficient algorithm for mining frequent rooted trees and free trees using canonical form. In: The 16th international conference on scientific and statistical database management, pp 11-20.
    • (2004) The 16th international conference on scientific and statistical database management , pp. 11-20
    • Chi, Y.1
  • 12
    • 21844454174 scopus 로고    scopus 로고
    • Canonical forms for labelled trees and their applications in frequent subtree mining
    • Chi Y et al (2005c) Canonical forms for labelled trees and their applications in frequent subtree mining. Knowl Inform Syst 8(2): 203-234.
    • (2005) Knowl Inform Syst , vol.8 , Issue.2 , pp. 203-234
    • Chi, Y.1
  • 13
    • 6344279447 scopus 로고    scopus 로고
    • Multi-relational data mining: An introduction
    • Džeroski S (2003) Multi-relational data mining: an introduction. SIGKDD Explor Newsl 5(1): 1-16.
    • (2003) SIGKDD Explor Newsl , vol.5 , Issue.1 , pp. 1-16
    • Džeroski, S.1
  • 14
    • 34548705372 scopus 로고    scopus 로고
    • 4th international workshop on mining software repositories (MSR 2007)
    • Gall H et al (2007) 4th international workshop on mining software repositories (MSR 2007). In: ICSE COMPANION '07, pp 107-108.
    • (2007) ICSE COMPANION '07 , pp. 107-108
    • Gall, H.1
  • 15
    • 34548757503 scopus 로고    scopus 로고
    • UNI3-efficient algorithm for mining unordered induced subtrees using TMG candidate generation
    • Hadzic F et al (2007) UNI3-efficient algorithm for mining unordered induced subtrees using TMG candidate generation. In: Computational intelligence and data mining, pp 568-575.
    • (2007) Computational Intelligence and Data Mining , pp. 568-575
    • Hadzic, F.1
  • 18
    • 47349094595 scopus 로고    scopus 로고
    • Knowledge discovery from XML documents
    • Springer, Berlin
    • Nayak R et al (2006) Knowledge discovery from XML documents. Lecture Notes in Computer Science, vol 3915. Springer, Berlin.
    • (2006) Lecture Notes in Computer Science , vol.3915
    • Nayak, R.1
  • 21
    • 85132288287 scopus 로고    scopus 로고
    • Parallel data mining for association rules on shared-memory systems
    • Parthasarathy S et al (2001) Parallel data mining for association rules on shared-memory systems. Knowl Inform Syst 3(1): 1-29.
    • (2001) Knowl Inform Syst , vol.3 , Issue.1 , pp. 1-29
    • Parthasarathy, S.1
  • 23
    • 23844545242 scopus 로고    scopus 로고
    • A high-performance distributed algorithm for mining association rules
    • Schuster A et al (2005) A high-performance distributed algorithm for mining association rules. Knowl Inform Syst 7(4): 458-475.
    • (2005) Knowl Inform Syst , vol.7 , Issue.4 , pp. 458-475
    • Schuster, A.1
  • 24
    • 0033320738 scopus 로고    scopus 로고
    • New algorithms for efficient mining of association rules
    • Shen L et al (1999) New algorithms for efficient mining of association rules. Inform Sci 118(1-4): 251-268.
    • (1999) Inform Sci , vol.118 , Issue.1-4 , pp. 251-268
    • Shen, L.1
  • 30
    • 19544369474 scopus 로고    scopus 로고
    • DRYADE: A new approach for discovering closed frequent trees in heterogeneous tree databases
    • Termier A et al (2004) DRYADE: a new approach for discovering closed frequent trees in heterogeneous tree databases. In: Proceedings of the 4th IEEE international conference on data mining, pp 543-546.
    • (2004) Proceedings of the 4th IEEE International Conference on Data Mining , pp. 543-546
    • Termier, A.1
  • 33
    • 2442458705 scopus 로고    scopus 로고
    • CrossMine: Efficient classification across multiple database relations
    • Yin X et al (2004) CrossMine: efficient classification across multiple database relations. In: International conference on data engineering, pp 399-410.
    • (2004) International Conference on Data Engineering , pp. 399-410
    • Yin, X.1
  • 34
    • 32344441804 scopus 로고    scopus 로고
    • Cross-relational clustering with user's guidance
    • Yin X et al (2005) Cross-relational clustering with user's guidance. In: Knowledge discovery and data mining, pp 344-353.
    • (2005) Knowledge Discovery and Data Mining , pp. 344-353
    • Yin, X.1
  • 35
    • 24044516553 scopus 로고    scopus 로고
    • Efficiently mining frequent embedded unordered trees
    • Zaki MJ (2005a) Efficiently mining frequent embedded unordered trees. Fundam Inform 66(1-2): 33-52.
    • (2005) Fundam Inform , vol.66 , Issue.1-2 , pp. 33-52
    • Zaki, M.J.1
  • 36
    • 24344486868 scopus 로고    scopus 로고
    • Efficiently mining frequent trees in a forest: Algorithms and applications
    • Zaki MJ (2005b) Efficiently mining frequent trees in a forest: algorithms and applications. IEEE Trans Knowl Data Eng 17(8): 1021-1035.
    • (2005) IEEE Trans Knowl Data Eng , vol.17 , Issue.8 , pp. 1021-1035
    • Zaki, M.J.1
  • 37
    • 36649030608 scopus 로고    scopus 로고
    • Discovering frequent agreement subtrees from phylogenetic data
    • Zhang S, Wang JTL (2008) Discovering frequent agreement subtrees from phylogenetic data. IEEE Trans Knowl Data Eng 20(1): 68-82.
    • (2008) IEEE Trans Knowl Data Eng , vol.20 , Issue.1 , pp. 68-82
    • Zhang, S.1    Wang, J.T.L.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.