Database Reference
In-Depth Information
25. E. Jaynes. On the rationale of maximum-entropy methods. Proc. IEEE , 70(9):939-952, 1982.
26. U. Kang and C. Faloutsos. Beyond caveman communities: Hubs and spokes for graph
compression and mining. In ICDM , pages 300-309. IEEE, 2011.
27. R. M. Karp. Reducibility among combinatorial problems. In Proc. Compl. Comp. Comput. ,
pages 85-103, New York, USA, 1972.
28. E. Keogh, S. Lonardi, and C. A. Ratanamahatana. Towards parameter-free data mining. In
KDD , pages 206-215, 2004.
29. E. Keogh, S. Lonardi, C. A. Ratanamahatana, L. Wei, S.-H. Lee, and J. Handley. Compression-
based data mining of sequential data. Data Min. Knowl. Disc. , 14(1):99-129, 2007.
30. P. Kontkanen and P. Myllymäki. A linear-time algorithm for computing the multinomial
stochastic complexity. Inf. Process. Lett. , 103(6):227-233, 2007.
31. P. Kontkanen, P. Myllymäki, W. Buntine, J. Rissanen, and H. Tirri. An MDL framework for
clustering. Technical report, HIIT, 2004. Technical Report 2004-6.
32. A. Koopman and A. Siebes. Discovering relational items sets efficiently. In SDM , pages
108-119, 2008.
33. A. Koopman and A. Siebes. Characteristic relational patterns. In KDD , pages 437-446, 2009.
34. H. T. Lam, F. Mörchen, D. Fradkin, and T. Calders. Mining compressing sequential patterns.
In SDM , 2012.
35. H. T. Lam, T. Calders, J. Yang, F. Moerchen, and D. Fradkin.: Mining compressing sequential
patterns in streams. In IDEA , pages 54-62, 2013.
36. M. van Leeuwen and A. Siebes. StreamKrimp: Detecting change in data streams. In ECML
PKDD , pages 672-687, 2008.
37. M. van Leeuwen, J. Vreeken, and A. Siebes. Compression picks the item sets that matter. In
PKDD , pages 585-592, 2006.
38. M. van Leeuwen, F. Bonchi, B. Sigurbjörnsson, and A. Siebes. Compressing tags to find
interesting media groups. In CIKM , pages 1147-1156, 2009.
39. M. van Leeuwen, J. Vreeken, and A. Siebes. Identifying the components. Data Min. Knowl.
Disc. , 19(2):173-292, 2009.
40. M. Li and P. Vitányi. An Introduction to Kolmogorov Complexity and its Applications . Springer,
1993.
41. M. Li, X. Chen, X. Li, B. Ma, and P. Vitanyi. The similarity metric. IEEE TIT , 50(12):
3250-3264, 2004.
42. C. Lucchese, S. Orlando, and R. Perego. Mining top-k patterns from binary datasets in presence
of noise. In SDM , pages 165-176, 2010.
43. M. Mampaey and J. Vreeken. Summarising categorical data by clustering attributes. Data Min.
Knowl. Disc. , 26(1):130-173, 2013.
44. M. Mampaey, J. Vreeken, and N. Tatti. Summarizing data succinctly with the most informative
itemsets. ACM TKDD , 6:1-44, 2012.
45. P. Miettinen and J. Vreeken. Model order selection for Boolean matrix factorization. In KDD ,
pages 51-59. ACM, 2011.
46. P. Miettinen and J. Vreeken. mdl4bmf: Minimum description length for Boolean matrix
factorization. ACM TKDD . In Press .
47. S. Papadimitriou, J. Sun, C. Faloutsos, and P. S. Yu. Hierarchical, parameter-free community
discovery. In ECML PKDD , pages 170-187, 2008.
48. B. Pfahringer. Compression-based feature subset selection. In Proc. IJCAI'95 Workshop on
Data Engineering for Inductive Learning , pages 109-119, 1995.
49. B. A. Prakash, J. Vreeken, and C. Faloutsos. Spotting culprits in epidemics: How many and
which ones? In ICDM. IEEE, 2012.
50. J. Quinlan. C4.5: Programs for Machine Learning . Morgan-Kaufmann, Los Altos, California,
1993.
51. L. D. Raedt. Declarative modeling for machine learning and data mining. In ECML PKDD ,
pages 2-3, 2012.
52. J. Rissanen. Modeling by shortest data description. Automatica , 14(1):465-471, 1978.
53. G. Schwarz. Estimating the dimension of a model. Annals Stat. , 6(2):461-464, 1978.
Search WWH ::




Custom Search