Parallel algorithms for mining frequent structural motifs in scientific data

Proceedings of the International Conference on Supercomputing

Volumn , Issue , 2004, Pages 31-40

  Wang, Chao ^a   Parthasarathy, Srinivasan ^a  

^a OHIO STATE UNIVERSITY   (United States)

Author keywords

Atomset; Backward pruning; Bitonic partitioning; Motif; Recursive fuzzy hashing; Self adaptive expansion

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; COSTS; DATA REDUCTION; FUZZY SETS; OPTIMIZATION;

CLUSTERS; DATASETS; SUBSTRUCTURES;

DATA MINING;

EID: 8344245353     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1006209.1006215     Document Type: Conference Paper

References (40)

1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In 20th VLDB Conf., September 1994.
2. C. Borgelt and M. Berthold. Mining molecular fragments: Finding relevant substructures of molecules. In IEEE International Conference on Data Mining, Dec 2002.
3. Douglas Burdick, Manuel Calimlim, and Johannes Gehrke. Mafia: A maximal frequent itemset algorithm for transactional databases. In IEEE International Conference on Data Engineering, 2001.
4. L. P. Chew, D. Huttenlocher, K. Kedem, and J. Kleinberg. Fast detection of common geometric substructures in proteins. RECOMB, 1999.
5. M. Coatney, S. Mehta, A. Choy, S. Barr, S. Parthasarathy, R. Machiraju, and J. Wilkins. Defect detection in silicon and alloys. In IEEE Workshop on Visualization in Bioinformatics and Cheminformatics, 2002.
6. M. Coatney and S. Parthasarathy. Motifminer: A general toolkit for efficiently identifying common substructures in molecules. In IEEE International Conference on Bioinformatics and Bioengineering (to appear), 2003.
7. M. Coatney and S. Parthasarathy. Motifminer: Efficient discovery of common substructures in biochemical molecules. In Knowledge and Information Systems, to appear, 2003.
8. D.J. Cook, L.B. Holder, S. Su, R. Maglothin, and I. Jonyer. Structural mining of molecular biology data. IEEE Engineering in Medicine and Biology, 20(4):67-74, 2001.
9. L. Dehaspe, H. Toivonen, and R. King. Finding frequent substructures in chemical compounds. In The Fourth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1998.
10. D.J.Cook and et al. L.B.Holder. Approaches to parallel and distributed computing. Journal of Parallel and Distributed Computing, 2001.
11. S. Djoko, D. Cook, and L. Holder. Analyzing the benefits of domain knowledge in substructure discovery. In The First ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1995.
12. U. Fayyad et al. Mining Scientific Data. In Communications of the ACM, pages 51-57, 1996.
13. H.M. Grindley, P.J. Artymiuk, D.W. Rice, and P. Willett. Identification of tertiary resemblence in proteins using a maximal common subgraph isomorphism algorithm. J. of Mol. Biol., 229(3):707-721, 1993.
14. E. Han, G. Karypis, and V. Kumar. Data mining for turbulent flows. In Data mining for scientific and engineering applications, pages 239-256, 2001.
15. D. Hand, H. Mannila, and P. Smyth. Principles of Data Mining. MIT Press, Cambridge, MA, 2001.
16. M. Jiang, T.-S. Choy, S. Mehta, M. Coatney, S. Barr, K. Hazzard, D. Richie, S. Parthasarathy, R. Machiraju, D. S. Thompson, J. Wilkins, and B. Gatlin. Feature Mining Algorithms for Scientific Data. In Proceedings of SIAM Data Mining Conference, May 2003.
17. I. Jonassen, I. Eidhammer, D. Conklin, and W. Taylor. Structure motif discovery and mining the pdb. In German Conference on Bioinformatics, 2000.
18. C. Kamath. On Mining Scientific Datasets. In et al R. L. Grossman, editor, Data Mining for Scientific and Engineering Applications, pages 1-21. Kluwer Academic Publishers, 2001.
19. G. Karypis, M. Deshpande, and M Kuramochi. Automated approaches for classifying structures. In BIOKDD02: Workshop on Data Mining in Bioinfomatics (with SIGKDD02 Conf.), July 2002.
20. J. Kim, E. Moriyama, C. Warr, P. Clyne, and J. Carlson. Identification of novel multi-transmembrane proteins from genomic databases using quasi-periodic structural properties. Bioinformatics, 2002.
21. R. King, A. Karwath, A. Clare, and L. Dehaspe. Genome scale prediction of protein functional class from sequence using data mining. In The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000.
22. S. Kirshner, I.V. Cadez, P. Smyth, and C. Kamath. Learning to classify galaxy shapes using the em algorithm. In ICPR, 2002.
23. I. Koch, T. Lengauer, and E. Wanke. An algorithm for finding maximal common subtopologies in a set of protein structures. J. of Comp. Biol., 3(2):289-306, 1996.
24. M Kuramochi and G. Karypis. Frequent subgraph discovery. In IEEE International Conference on Data Mining, Nov 2001.
25. M Kuramochi and G. Karypis. Discovering frequent geometric subgraphs. In IEEE International Conference on Data Mining, Dec 2002.
26. Y. Lamdan and H. Wolfson. Geometric hashing: a general and efficient model-based recognition scheme. In ICCV, 1988.
27. H. Li and S. Parthasarathy. Automatically deriving multi-level protein structures through data mining. In HiPC Conference Workshop on Bioinformatics and Computational Biology, Hyderabad, India, 2001.
28. R. Machiraju, S. Parthasarathy, D. S. Thompson, J. Wikins, B. Gatlin, T. S. Choy, D. Richie, M. Jiang, S. Mehta, M. Coatney, S. Barr, and K. Hazzard. Mining Complex Evolutionary Phenomena. In NSF NGDM Workshop, 2002.
29. R. Machiraju, S. Parthasarathy, D. S. Thompson, J. Wikins, B. Gatlin, T. S. Choy, D. Richie, M. Jiang, S. Mehta, M. Coatney, S. Barr, and K. Hazzard. Mining Complex Evolutionary Phenomena. In H. Kargupta et al., editor, Data Mining for Scientific and Engineering Applications. MIT Press, 2003.
30. E.M. Mitchell, P.J. Artymiuk, D.W. Rice, and P. Willett. Use of techniques derived from graph theory to compare secondary structure motifs in proteins. J. Mol. Biol., 212:151-166, 1990.
31. W. Pan, J. Lin, and C. Le. Model-based cluster analysis of microarray gene-expression data. Genome Biology, 2002.
32. S. Parthasarathy and M. Coatney. Efficient discovery of common substructures in macromolecules. In IEEE International Conference on Data Mining, 2002.
33. L. De Raedt and S. Kramer. The level-wise version space algorithm and its application to molecular fragment finding. In Seventeenth International Joint Conference on Artificial Intelligence, 2001.
34. M. Twa, S. Parthasarathy, T. Rosche, and M. Bullmer. Decision tree classification of spatial data patterns from videokeratography using zernike polynomials. SIAM International Conference on Data Mining, 2003.
35. J. T. L. Wang, B. A. Shapiro, D. Shasha, K. Zhang, and C.-Y. Chang. Automated discovery of active motifs in multiple rna secondary structures. In International Conference on Knowledge Discovery and Data Mining, 1996.
36. X. Wang, J. Wang, D. Shasha, B. Shapiro, S. Dikshitulu, I. Rigoutsos, and K. Zhang. Automated discovery of active motifs in three dimensional molecules. In The Third ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1997.
37. X. Wang, J.T.L. Wang, D. Shasha, B.A. Shapiro, I. Rigoutsos, and K. Zhang. Finding patterns in three-dimensional graphs: Algorithms and applications to scientific data mining. IEEE Transactions on Knowledge and Data Engineering, 14(4):731-749, jul/aug 2002.
38. H. Wolfson and I. Rigoutsos. Geometric hashing: An overview. In In IEEE Computational Science and Engineering, Oct 1997.
39. X. Yan and J. Han. gspan: Graph based substructure pattern mining. In IEEE International Conference on Data Mining, Dec 2002.
40. X. Zheng and T. Chan. Chemical genomics: A systematic approach in biological research and drug discovery. Current Issues in Molecular Biology, 2002.