Maximum Entropy modelling for assessing results on real-valued data

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2011, Pages 350-359

  Kontonasios, Kleanthis Nikolaos ^a   Vreeken, Jilles ^b   De Bie, Tijl ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b UNIVERSITY OF ANTWERP   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYTICAL MODEL; BACKGROUND MODEL; BASIC PROPERTIES; CONVEX OPTIMIZATION TECHNIQUES; HISTOGRAM ESTIMATOR; HYPOTHESIS TESTING; KNOWLEDGE DISCOVERY PROCESS; MARGINAL DISTRIBUTION; MAXIMUM ENTROPY; MAXIMUM ENTROPY PRINCIPLE; MISSING VALUES; PATTERN MINING; RANDOMISATION; REAL-VALUED DATA; SAMPLE DATA; STATISTICAL ASSESSMENT;

DATA MINING;

INTRUSION DETECTION;

EID: 84857168455     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2011.98     Document Type: Conference Paper

References (17)

1. U. Alon, N. Barkai, D.A. Notterman, K. Gish, S. Ybarra, D. Mack, and A.J. Levine. Data pertaining to the article broad patterns of gene expression revealed by clustering of tumor and normal colon tissues probed by oligonucleotide arrays. In Proc. Natl. Acad. Sci., volume 96, pages 6745-6750, 1999.
2. D.P. Bertsekas. Nonlinear Programmng. Athena Scientific, 1999.
3. T. De Bie. Maximum entropy models and subjective interestingness: an application to tiles in binary databases. Data Mining and Knowledge Discovery, 23(3):407-446, 2011.
4. T. De Bie, K.-N. Kontonasios, and E. Spyropoulou. A framework for mining interesting pattern sets. SIGKDD Expl., 12(2):92-100, 2010.
5. A. Gionis, H. Mannila, T. Mielikäinen, and P. Tsaparas. Assessing data mining results via swap randomization. ACM Trans. Knowl. Discov. Data, 1(3), 2007.
6. P.D. Grünwald. The Minimum Description Length Principle. MIT Press, 2007.
7. 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 Proc. KDD'09, pages 379-388, 2009.
8. E.T. Jaynes. On the rationale of maximum-entropy methods. Proc. of the IEEE, 70(9):939-952, 1982.
9. A.J. Knobbe and E.K.Y. Ho. Maximally informative κ- itemsets and their efficient discovery. In Proc. KDD'06, pages 237-244, 2006.
10. P. Kontkanen and P. Myllymäki. MDL histogram density estimation. In Proc. AISTATS'07, 2007.
11. K.-N. Kontonasios and T. De Bie. An informationtheoretic approach to finding informative noisy tiles in binary databases. In Proc. SDM'10, pages 153-164, 2010.
12. M. Ojala. Assessing data mining results on matrices with randomization. In Proc. ICDM'10, pages 959-964, 2010.
13. M. Ojala, N. Vuokko, A. Kallio, N. Haiminen, and H. Mannila. Randomization of real-valued matrices for assessing the significance of data mining results. In Proc. SDM'08, pages 494-505, 2008.
14. A. Prelic, S. Bleuler, P. Zimmermann, A. Wille, P. Buhlmann, W. Gruissemand, L. Hennig, L. Thiele, and E. Zitzler. A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinf., 22(9):1122-1129, 2006.
15. U. Scherf, D.T. Ross, M. Waltham, L.H. Smith, J.K. Lee, L. Tanabe, K.W. Kohn, W.C. Reinhold, T.G. Myers, D.T. Andrews, D.A. Scudiero, M.B. Eisen, E.A. Sausville, Y. Pommier, D. Botstein, P.O. Brown, and J.N. Weinstein. A gene expression database for the molecular pharmacology of cancer. Nat. Genet., 24:236-244, 2000.
16. L. Todorovski, P. Flach, and N. Lavrač. Predictive performance of weighted relative accuracy. In Proc. PKDD'00, pages 255-264, 2000.
17. M. Wainwright and M.I. Jordan. Graphical models, exponential families, and variational inference. Foun. Trends Mach. Learn., 1(1-2):1-305, 2008.