AMIOT: Induced ordered tree mining in tree-structured databases

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2005, Pages 170-177

  Hido, Shohei ^a   Kawano, Hiroyuki ^b  

^a KYOTO UNIVERSITY   (Japan)

^b NANZAN UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA MINING; DATABASE SYSTEMS; REDUNDANCY; XML;

AMIOT; CANDIDATE TREES; FREQT ALGORITHM; TREE MINING;

DECISION TREES;

EID: 34548549002     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2005.20     Document Type: Conference Paper

References (16)

1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB'94), pages 487499,1994.
2. Apache. Xindice. http://xml.apache.org/xindice, 2002.
3. T. Asai, K. Abe, S. Kawasoe, H. Arimura, H. Sakamoto, and S. Arikawa. Efficient substructure discovery from large semi-structured data. In Proceedings of the 2nd SIAM International Conference on Data Mining (SDM'02), pages 158-174, 2002.
4. T. Asai and H. Arimura. Algorithms for mining semistructured data. IEICE Journal, J87-D1(2):79-96, 2004. (in Japanese).
5. R. J. Bayardo Jr. Efficiently mining long patterns from databases. In Proceedings of the International Conference on Management of data(ACM SIGMOD'98), pages 85-93, 1998.
6. Y. Chi, Y. Yang, and R. R. Muntz. Hybridtreeminer: An efficient algorithm for mining frequent rooted trees and free trees using canonical form. In Proceedings of the 16th International Conference on Scientific and Statistical Database Management (SSDBM'04), pages 11-20, 2004.
7. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In Proceedings of the 2000 International Conference on Management of Data (ACM SIGMOD'00), pages 1-12, 2000.
8. S. Hido and H. Kawano. Proposal of efficient enumeration method for frequent ordered subtree discovery. DBSJ Letters, 4(1): 161-164, 2005. (in Japanese).
9. IBM alphaWorks. ToXgene - the ToX XML Data Generator. http://www.alphaworks.ibm.com/tech/toxgene, 2002.
10. A. Inokuchi, T. Washio, and H. Motoda. An apriori-based algorithm for mining frequent substructures from graph data. In Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD'00), pages 13-23, 2000.
11. A. Inokuchi, T. Washio, and H. Motoda. Applying the apriori-based graph mining method to mutagenesis data analysis. Computer Aided Chemistry, 2:87-92, 2001.
12. T. Kudo and Y. Matsumoto. A boosting algorithm for classification of semi-structured text. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP'04), pages 301-308, 2004.
13. S. Nijssen. Frequent structure mining: Efficiency issues. In the 2nd International Workshop on Mining Graphs, Trees and Sequences (MGTS'04), 2004. http://hms.liacs.nl/mgts2004/mgts-intro.pdf.
14. J. R. Punin, M. S. Krishnamoorthy, and M. J. Zaki. Web usage mining: Languages and algorithms. Technical Report 03-1, Rensselaer Polytechnic Institute, 2001.
15. C. Wang, M. Hong, J. Pei, H. Zhou, W. Wang, and B. Shi. Efficient pattern-growth methods for frequent tree pattern mining. In Proceedings of the 8th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD'04), pages 441-451, 2004.
16. M. J. Zaki. Efficiently mining frequent trees in a forest. In Proceedings of the 8th International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD '02), pages 71-80, 2002.