Abstract
Data Mining and Knowledge Discovery is a young but vigorously growing research area. Its aim is to discover structure or knowledge in databases. It comprises a wide variety of algorithms and techniques for towards this goal.
One of the main challenges in building a data mining system is the flexibility necessary both to support the current variety of algorithms and to extend it easily with new kinds of data mining algorithms. In the Keso project this challenge is met by basing the system on an Inductive Query Language.
This research is supported by ESPRIT under contract 20.596
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References
S. Abiteboul, R. Hull, and V. Vianu. Foundations of Databases. Addison Wesley, 1994.
R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of the 1993 International Conference on Management of Data (SIGMOD 93), pages 207–216, May 1993.
C. Bishop. Neural Networks for Pattern Recognition. Clarendon Press, 1995.
L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth, 1984.
P. Cheeseman and J. Stutz. Bayesian Classification (Autoclass): Theory and Results, pages 153–180. In Fayyad et al. [10], 1996.
B. S. Duran and P. L. Odell. Cluster Analysis, A Survey. Lecture Notes in Economics and Mathematical Systems, vol 100. Springer-Verlag, 1974.
B. Efron and R. J. Tibshirani. An Introduction to the Bootstrap. Monographs on Statistics and Applied probability, vol 57. Chapman & Hall, 1993.
U. M. Fayyad. Branching on attribute values in decision tree generation. In Proceedings of the 12th National Conference on Artificial Intelligence, pages 601–606. AAAI/MIT Press, 1994.
U. M. Fayyad, G. Piatetsky-Shapiro, and P. Smyth. From Data Mining to Knowledge Discovery: An Overview, pages 1–34. In Fayyad et al. [10], 1996.
U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996.
U. M. Fayyad and R. Uthurusamy, editors. AAAI-95 Conference on Knowledge Discovery and Data Mining, Montreal, Quebec, 1995.
A. Feelders. Learning from biased data using mixture models. In Simoudis et al. [31], pages 102–107.
W. Frawley, G. Piatetsky-Shapiro, and C. Matheus. Knowledge Discovery in Databases: An Overview, pages 1–27. In Piatetsky-Shapiro and Frawley [25], 1991.
J. Friedman and J. Tukey. A projection pursuit algorithm for exploratory data analysis. IEEE Transactions on Computing, C-23:881–889, 1974.
J. Hertz, A. Krogh, and R. G. Palmer. Introduction to the Theory of Neural Networks. Santa Fe Institute Lecture Notes vol 1. Addison-Wesley, 1991.
M. Holsheimer, M. Kersten, and A. Siebes. Data surveyor: Searching the nuggets in parallel. In Fayyad et al. [10].
P. J. Huber. Projection pursuit. The Annals of Statistics, 13(2):435–475, 1985.
W. Klösgen. Explora: A multipattern and multistrategy discovery assistent. In Fayyad et al. [10].
J. R. Koza. Genetic programming, volume 1. MIT Press, 1992.
J. R. Koza. Genetic programming, volume 2. MIT Press, 1994.
H. Mannila and K.-J. Räihä. Algorithms for inferring functional dependencies from relations. Data and Knowledge Engineering, 12:83–99, 1994.
K. Mardia, J. Kent, and J. Bibby. Multivariate Analysis. Probability and Mathematical Statistics. Academic Press, 1979.
C. J. Mateus, G. Piatetsky-Shapiro, and D. McNeill. Selecting and reporting what is interesting: The kefir application to healthcare data. In Fayyad et al. [10].
D. Michie, D. Spiegelhalter, and C. Taylor, editors. Machine Learning, Neural and Statistical Classification. Ellis Horwood series in Artificial Intelligence. Ellis Horwood, 1994.
G. Piatetsky-Shapiro and W. J. Frawley, editors. Knowledge Discovery in Databases. AAAI Press, Menlo Park, California, 1991.
J. Quinlan. Induction of decision trees. Machine Learning, 1:81–106, 1986.
J. Quinlan. Probabilistic decision trees. In Y. Kodratoff and R. Michalski, editors, Machine Learning: An Artificial Intelligence Approach, Vol 3. Morgan Kaufmann, 1990.
B. Ripley. Pattern Recognition and Neural Networks. Cambridge University Press, 1996.
A. Siebes. Data surveying, foundations of an inductive query language. In Fayyad and Uthurusamy [11], pages 269–274.
A. Siebes. On the inseparability of data mining and statistics. In Proceedings of the Mlnet Familiarization Workshop: Statistics, Machine Learning and Knowledge Discovery in Databases, 1995.
E. Simoudis, J. Han, U. M. Fayyad, and R. Uthurusam, editors. AAAI-96 Conference on Knowledge Discovery and Data Mining, Portland, Oregon, 1996.
J. Tukey. Exploratory Data Analysis. Addison-Wesley, 1977.
D. H. Wolpert and W. G. Macready. No free lunch theorems for search. Technical Report SFI-TR-95-02-10, The Santa Fe Institute, Februari 1996.
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Siebes, A. (1996). Data Mining and the Keso project. In: Jeffery, K.G., Král, J., Bartošek, M. (eds) SOFSEM'96: Theory and Practice of Informatics. SOFSEM 1996. Lecture Notes in Computer Science, vol 1175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0037403
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DOI: https://doi.org/10.1007/BFb0037403
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