Data mining in inductive databases

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3933 LNCS, Issue , 2006, Pages 1-23

  Siebes, Arno ^a  

^a UTRECHT UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

DATA STRUCTURES; DATABASE SYSTEMS; KNOWLEDGE ACQUISITION; PATTERN RECOGNITION;

DATA MINING LITERATURE; INDUCTIVE DATABASES; OPERATIONAL DEFINITION;

DATA MINING;

EID: 33745787494     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11733492_1     Document Type: Conference Paper

References (39)

1. R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proc. ACM SIGMOD conference., pages 207-216, 1993.
2. Rakesh Agrawal, Christos Faloutsos, and Arun N. Swami. Efficient similarity search in sequence databases. In D. Lomet, editor, Proceedings of the 4th International Conference of Foundations of Data Organization and Algorithms, pages 69-84. Springer Verlag, 1993.
3. Andrea Asperti and Giuseppe Longo. Categories, Types, and Structures. MIT Press, 1991.
4. Ronnie Bathoorn and Arno Siebes. Discovering (almost) phylogentic trees from developmental sequences data. In Knowledge Discovery in Databases, PKDD2004, volume 3202 of Lecture Notes in AI.
5. Francesco. Bonchi and Claudio Lucchese. On closed constrained frequent pattern mining. In Rajeev Rastogi, Katharina Morik, Max Bramer, and Xindong Wu, editors, Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM'04), pages 35-42, 2004.
6. Jean-François Boulicaut and Artur Bykowski. Frequent closures as a concise representation for binary data mining. In Knowledge Discovery and Data Mining, Current Issues and New Applications, 4th Pacific-Asia Conference, PADKK 2000, pages 62-73, 2000.
7. Robert Castelo, Ad Feelders, and Arno Siebes. Mambo: Discovering association rules based on conditional independencies. In Advances in Intelligent Data Analysis, 4th International Conference, IDA 2001, pages 289-298. Springer Verlag, 2001.
8. David J. Hand. Pattern detection and discovery. In Pattern Detection and Discovery, volume 2447 of Lecture Notes in AI, pages 1-12. Springer Verlag, 2002.
9. Jaakko Hollmén, Jouni K. Seppanen, and Heikki Mannila. Mixture models and frequent sets: Combining global and local methods for 0-1 data. In Proc. SIAM Conference on Data Mining (SDM) 2003, 2003.
10. Pedrito Maynard-Reid II and Urszula Chajewska. Aggregating learned probabilistic beliefs. In Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, pages 354-361. Morgan Kaufmann, 2001.
11. Tomasz Imielinski and Heikki Mannila. A database perspective on knowledge discovery. Communications of the ACM, 39(11):58-64, 1996.
12. E.T. Jaynes. Probability Theory: The Logic of Science.
13. Hillol Kargupta and Philip Chan, editors. Advances in Distributed and Parallel Knowledge Discovery. MIT Press, 2000.
14. Arno J. Knobbe, Marc de Haas, and Arno Siebes. Propositionalisation and aggregates. In Luc De Raedt and Arno Siebes, editors, Principles of Data Mining and Knowledge Discovery (PKDD), volume 2168 of Lecture Notes in Computer Science, pages 277-288. Springer Verlag, 2001.
15. Isaac S. Kohane, Alvin T. Kho, and Atul J. Butte. Microarrays for an Integrative Genomics. Computational Molecular Biology. MIT Press, 2003.
16. Krogel, S. Rawles, F. Zelezny, P. Flach, N. Lavrac, and S. Wrobel. Comparative evaluation of approaches to propositionalization. In Tamas Horvath and Akihiro Yamamoto, editors, Proceedings of the 13th International Conference on Inductive Logic Programming (ILP'2003), pages 194-217. Springer-Verlag, October 2003.
17. Ludmila Kuncheva. Combining Pattern Classifiers: Methods and Algorithms. John Wiley & Sons, 2004.
18. Mark Last, Abraham Kandel, and Horst Bunke, editors. Data Mining in Time Series Databases. World Scientific, 2004.
19. Bing Liu, Wynne Hsu, and Yiming Ma. Integrating classification and association rule mining. In Proc. of the ACM KDD conference, pages 80-86, 1998.
20. H. Mannila, H. Toivonen, and A.I. Verkamo. Discovery of frequent episodes in event sequences, volume 1, pages 259-289, 1997.
21. Geoffrey J. McLachlan and Thriyambakam Krishnan. The EM Algorithm and Extensions. Wiley Series in Probability and Statistics. John Wiley & Sons, 1997.
22. Katharina Morik, Jean-François Boulicaut, and Arno Siebes. Preface. In Local Pattern Detection, volume 3539 of Lecture Notes in AI. Springer Verlag, 2005.
23. S. Naqvi and S. Tsur. A Logical Language for Data and Knowledge Bases. Computer Science Press, 1989.
24. Richard E. Neapolitan. Learning Bayesian Networks. Prentice Hall, 2003.
25. Raymond T. Ng, Laks V. S. Lakshmanan, Jiawei Han, and Alex Pang. Exploratory mining and pruning optimizations of constrained associations rules. In Proc. ACM SIGMOD conference, 1998.
26. Raymond T. Ng, Laks V. S. Lakshmanan, Jiawei Han, and Alex Pang. Exploratory mining and pruning optimizations of constrained associations rules. In Proceedings of 1998 ACM SIGMOD International Conference Management of Data, pages 13-24, 1998.
27. Nicolas Pasquier, Yves Bastide, Rafik Taouil, and Lotfi Lakhal. Discovering frequent closed itemsets for association rules. In Proceedings of 7th ICDT, pages 398-416, 1999.
28. Dmitry Pavlov, Heikki Mannila, and Padhraic Smyth. Beyond independence: Probabilistic models for query approximation on binary transaction data. Technical Report UCI-ICS TR-01-09, UC Irvine, 2001.
29. Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kauffman, 1997.
30. Luc De Raedt. A perspective on inductive databases. SIGKDD Explorations, 4(2):69-77, 2000.
31. Luc De Raedt and Kristian Kersting. Probabilistic logic learning. SIGKDD Explorations, 5(1):31-48, 2003.
32. Håvard Rue and Leonhard Held. Gaussian Markov Random Fields, volume 104 of Monographs on Statistics and Applied Probablity. Chapman & Hall/CRC, 2005.
33. Charles Semple and Mike Steel. Phylogenetics, volume 24 of Oxford Lecture Series in Mathematics and its Applications. Oxford University Press, 2003.
34. John Shaw-Taylor and Nello Cristianini. Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.
35. Arno Siebes, Jilles Vreeken, and Matthijs van Leeuwen. Item sets that compress. In Proceedings of the SIAM conference on Data Mining (SDM), 2006.
36. Zbyszek Struzik and Arno Siebes. The haar wavelet transform in the time series similarity paradigm. In Jan M. Zytkow and Jan Rauch, editors, Principles of Data Mining and Knowledge Discovery (PKDD), volume 1704 of Lecture Notes in AI, pages 12-22. Springer Verlag, 1999.
37. Giorgio Valentini and Francesco Masulli. Ensembles of learning machinesensembles of learning machines. In Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers, pages 3-22. Springer Verlag, 2002.
38. Shalom M. Weiss, Nitin Indurkhya, Tong Zhang, and Fred J. Damerau. Text Mining. Springer Verlag, 2005.
39. Osmar R. Zaiane, Simeon Simoff, and Chabane Djeraba, editors. Mining Multimedia and Complex Data, volume 2797 of Lecture Notes in AI. Springer Verlag, 2003.