Frequent subgraph mining in outerplanar graphs

Data Mining and Knowledge Discovery

Volumn 21, Issue 3, 2010, Pages 472-508

  Horváth, Tamás ^a,c   Ramon, Jan ^b   Wrobel, Stefan ^a,c  

^a UNIVERSITY OF BONN   (Germany)

^b UNIVERSITY OF LEUVEN   (Belgium)

^c FRAUNHOFER IAIS   (Germany)

Author keywords

Algorithms; Applications; Complexity; Frequent pattern mining; Graph mining

Indexed keywords

ARBITRARY GRAPHS; BIOINFORMATICS APPLICATIONS; COMPLEXITY; DATA SETS; FREQUENT PATTERN DISCOVERY; FREQUENT PATTERN MINING; FREQUENT SUBGRAPH MINING; GRAPH CLASS; GRAPH MINING; HEURISTIC STRATEGY; LARGE DATABASE; OUTERPLANAR GRAPH; POLYNOMIAL-TIME; SEARCH SPACES; SUBGRAPH MINING;

ALGORITHMS; BIOINFORMATICS; GRAPHIC METHODS; POLYNOMIAL APPROXIMATION;

TREES (MATHEMATICS);

EID: 77958077680     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-009-0162-1     Document Type: Article

References (41)

1. Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo AI (1996) Fast discovery of association rules. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining. AAAI Press/The MIT Press, Menlo Park, CA, pp 307-328
2. Bodlaender HL (1998) A partial κ-arboretum of graphs with bounded treewidth. Theor Comput Sci 209 (1-2):1-45
3. Borgelt C, Berthold M (2002) Mining molecular fragments: Finding relevant substructures of molecules. In: Proceedings of the 2002 IEEE international conference on data mining (ICDM). IEEE Computer Society, pp 51-58
4. Calders T, Ramon J, Van Dyck D (2008) Anti-monotonic overlap-graph support measures. In: Proceedings of the 2008 IEEE international conference on data mining (ICDM). IEEE Computer Society, pp 73-82
5. Chartrand G, Harary F (1967) Planar permutation graphs. Annales de l'institut Henri Poincaré, (Sec. B) Probabilités et Statistiques 3(4):433-438
6. ChiY,Nijssen S,Muntz RR,Kok JN (2005) Frequent subtreemining-an overview. Fundam Inform 66:161-198
7. Chi Y, Yang Y, Muntz RR (2005) Canonical forms for labelled trees and their applications in frequent subtree mining. Knowl Inf Syst 8(2):203-234
8. Cook D, Holder L (1994) Substructure discovery using minimum description length and background knowledge. J Artif Intell Res 1:231-255
9. Deshpande M, KuramochiM,Wale N, Karypis G (2005) Frequent substructure-based approaches for classifying chemical compounds. IEEE Trans Knowl Data Eng 17(8):1036-1050
10. Diestel R (2005) Graph theory. 3. Springer, Heidelberg
11. Feder T, Hell P (1998) List homomorphisms to reflexive graphs. J Comb Theory B 72(2):236-250
12. Garey MR, Johnson DS (1979) Computers and intractability: A guide to the theory of NP-completeness. Freeman, San Francisco
13. Harary F (1971) Graph theory. Addison-Wesley, Reading
14. He H, Singh AK (2007) Efficient algorithms for mining significant substructures in graphs with quality guarantees. In: Proceedings of the 2007 IEEE international conference on data mining (ICDM). IEEE Computer Society, pp 163-172
15. Hedetniemi S, Chartrand G, Geller D (1971) Graphs with forbidden suhgraphs. J Comb Theory 10:12-41
16. Hopcroft JE, Wong JK (1974) Linear time algorithm for isomorphism of planar graphs. In: Proceedings of the sixth annual ACM symposium on theory of Computing (STOC). ACM Press, New York, pp 172-184
17. Horváth T (2005) Cyclic pattern kernels revisited. In: Proceedings of the 9th Pacific-Asia conference on advances in knowledge discovery and data mining (PAKDD), vol 3518 of LNAI. Springer, Heidelberg, pp 791-801
18. Horváth T, Bringmann B, Raedt LD (2007) Frequent hypergraph mining. In: Proceedings of the 16th international conference on inductive logic programming (ILP), vol 4455 of LNAI. Springer, Heidelberg, pp 244-259
19. Horváth T, Wrobel S, Bohnebeck U (2001) Relational instance-based learning with lists and terms. Mach Learn 43(1/2):53-80
20. Inokuchi A,Washio T, Motoda H (2003) Complete mining of frequent patterns from graphs: Mining graph data. Mach Learn 50(3):321-354
21. Johnson DS, Papadimitriou CH, YannakakisM (1988) On generating all maximal independent sets. Inform Process Lett 27(3):119-123
22. Koontz W (1980) Economic evaluation of loop feeder relief alternatives. Bell Syst Tech J 59:277-281
23. Kramer S, De Raedt L, Helma C (2001) Molecular feature mining in HIV data. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining. ACM Press, New York, pp 136-143
24. Kuramochi M, Karypis G (2001) Frequent subgraph discovery. In: Proceedings of the 2001 international conference on data mining (ICDM). IEEE Computer Society, pp 313-320
25. Leydold J, Stadler PF (1998) Minimal cycle bases of outerplanar graphs. Electron J Comb 5
26. Lingas A (1989) Subgraph isomorphism for biconnected outerplanar graphs in cubic time. Theor Comput Sci 63(3):295-302
27. Mannila H, Toivonen H (1997) Levelwise search and borders of theories in knowledge discovery. Data Mining Knowl Discover 1(3):241-258
28. Matula DW (1978) Subtree isomorphism in O(n5/2). Ann Discrete Math 2:91-106
29. Maunz A, Helma C, Kramer S (2009) Large-scale graph mining using backbone refinement classes. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM Press, New York, pp 617-626
30. Mitchell SL (1979) Linear algorithms to recognize outerplanar and maximal outerplanar graphs. Inform Process Lett 9(5):229-232
31. Nijssen S, Kok JN (2004) A quickstart in frequent structure mining can make a difference. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. ACM Press, New York, pp 647-652
32. Nishi T, Chua LO (1986) Uniqueness of solution for nonlinear resistive circuits containing CCCS's or VCVS's whose controlling coefficients are finite. IEEE Trans Circuits Syst 33(4):381-397
33. Ramon J, Nijssen S (2009) Polynomial-delay enumeration of monotonic graph classes. J Mach Learn Res 10:907-929
34. Read RC, Tarjan RE (1975) Bounds on backtrack algorithms for listing cycles, paths, and spanning trees. Networks 5(3):237-252
35. Schietgat L, Costa F, Ramon J, De Raedt L (2009) Maximum common subgraphmining: A fast and effective approach towards feature generation. In: Proceedings of the 7th international workshop on mining and learning with graphs (MLG). Leuven, Belgium, pp 1-3
36. Schietgat L, Ramon J, Bruynooghe M, Blockeel H (2008) An efficiently computable graph-basedmetric for the classification of small molecules. In: Proceedings of the 11th international conference on discovery science (DS), vol 5255 of LNAI. Springer, Heidelberg, pp 197-209
37. Shamir R, Tsur D (1999) Faster subtree isomorphism. J Algorithms 33(2):267-280
38. SysloMM (1982) The subgraph isomorphism problem for outerplanar graphs. Theor Comput Sci 17:91-97
39. Tarjan RE (1972) Depth-first search and linear graph algorithms. SIAM J Comput 1(2):146-160
40. Tong H, Faloutsos C, Gallagher B, Eliassi-Rad T (2007) Fast best-effort pattern matching in large attributed graphs. In: Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining. ACM Press, New York, pp 737-746
41. Yan X, Han J (2002) gSpan: graph-based substructure pattern mining. In: Proceedings of the 2002 IEEE international conference on data mining. IEEE Computer Society, pp 721-724