Database Reference
In-Depth Information
25. W. Hämäläinen. Kingfisher: an efficient algorithm for searching for both positive and negative
dependency rules with statistical significance measures. Knowl. Inf. Sys. , 32(2):383-414, 2012.
26. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In SIGMOD ,
pages 1-12. ACM, 2000.
27. D. Hand, N. Adams, and R. Bolton, editors. Pattern Detection and Discovery . Springer-Verlag,
2002.
28. S. Hanhijärvi, G. C. Garriga, and K. Puolamäki. Randomization techniques for graphs. In
SDM , pages 780-791. SIAM, 2009.
29. S. Hanhijärvi, M. Ojala, N. Vuokko, K. Puolamäki, N. Tatti, and H. Mannila. Tell me something
I don't know: randomization strategies for iterative data mining. In KDD , pages 379-388. ACM,
2009.
30. H. Heikinheimo, J. K. Seppänen, E. Hinkkanen, H. Mannila, and T. Mielikäinen. Finding
low-entropy sets and trees from binary data. In KDD , pages 350-359, 2007.
31. H. Heikinheimo, J. Vreeken, A. Siebes, and H. Mannila. Low-entropy set selection. In SDM ,
pages 569-580, 2009.
32. A. Henelius, J. Korpela, and K. Puolamäki. Explaining interval sequences by randomization.
In ECML PKDD , pages 337-352. Springer, 2013.
33. IBM. IBM Intelligent Miner User's Guide, Version 1, Release 1 , 1996.
34. S. Jaroszewicz and D. A. Simovici. Interestingness of frequent itemsets using bayesian networks
as background knowledge. In KDD , pages 178-186. ACM, 2004.
35. E. Jaynes. On the rationale of maximum-entropy methods. Proc. IEEE , 70(9):939-952, 1982.
36. R. M. Karp. Reducibility among combinatorial problems. In Proc. Compl. Comp. Comput. ,
pages 85-103, New York, USA, 1972.
37. K.-N. Kontonasios and T. De Bie. An information-theoretic approach to finding noisy tiles in
binary databases. In SDM , pages 153-164. SIAM, 2010.
38. K.-N. Kontonasios and T. De Bie. Formalizing complex prior information to quantify subjective
interestingness of frequent pattern sets. In IDA , pages 161-171, 2012.
39. J. Lijffijt, P. Papapetrou, and K. Puolamäki. A statistical significance testing approach to mining
the most informative set of patterns. Data Min. Knowl. Disc. , pages 1-26, 2012.
40. C. Lucchese, S. Orlando, and R. Perego. Mining top-k patterns from binary datasets in presence
of noise. In SDM , pages 165-176, 2010.
41. M. Mampaey. Mining non-redundant information-theoretic dependencies between itemsets.
In DaWaK , pages 130-141, 2010.
42. M. Mampaey, J. Vreeken, and N. Tatti. Summarizing data succinctly with the most informative
itemsets. TKDD , 6:1-44, 2012.
43. H. Mannila and H. Toivonen. Multiple uses of frequent sets and condensed representations. In
KDD , pages 189-194, 1996.
44. H. Mannila, H. Toivonen, and A. I. Verkamo. Efficient algorithms for discovering association
rules. In KDD , pages 181-192, 1994.
45. H. Mannila, H. Toivonen, and A. I. Verkamo. Levelwise search and borders of theories in
knowledge discovery. Data Min. Knowl. Disc. , 1(3):241-258, 1997.
46. R. Meo. Theory of dependence values. ACM Trans. Database Syst. , 25(3):380-406, 2000.
47. P. Miettinen and J. Vreeken. Model order selection for Boolean matrix factorization. In KDD ,
pages 51-59. ACM, 2011.
48. P. Miettinen and J. Vreeken. MDL4BMF: Minimum description length for Boolean matrix
factorization. Technical Report MPI-I-2012-5-001, Max Planck Institute for Informatics, 2012.
49. P. Miettinen, T. Mielikäinen, A. Gionis, G. Das, and H. Mannila. The discrete basis problem.
IEEE TKDE , 20(10):1348-1362, 2008.
50. F. Moerchen, M. Thies, and A. Ultsch. Efficient mining of all margin-closed itemsets with
applications in temporal knowledge discovery and classification by compression. Knowl. Inf.
Sys. , 29(1):55-80, 2011.
51. M. Ojala. Assessing data mining results on matrices with randomization. In ICDM , pages
959-964, 2010.
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