Abstract
It has been felt for some time that, despite employing different formalisms, being served by their own dedicated research communities and addressing distinct issues of practical interest, problems in Data Mining and Machine Learning connect through deep relationships. The paper [5] has taken a first step towards linking Data Mining and Machine Learning via Combinatorics by showing a correspondence between the problem of finding maximally specific sentences that are interesting in a database, the model of exact learning of monotone boolean functions in computational learning theory and the hyper-graph transversal problem in the combinatorics of finite sets. [5] summarises and concludes a series of valuable Data Mining research on fast discovery of association rules by the levelwise algorithm, series that includes [1, 4, 11]. Intuitively, a Data Mining task may consist of finding many weak predictors in a hypothesis space whereas in Machine Learning one strong predictor is sought.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. Verkamo. Fast Discovery of Association Rules. In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advance in Knowledge Discovery and Data Mining, pages 307-328. AAAI Press/MIT Press, 1996.
S. Anthony and A. Frisch. Cautious Induction in Inductive Logic Programming. In Lavraˇc and Dˇzeroski [9], pages 45-60.
A. Blumer, A. Ehrenfeucht, D. Haussler, and M.K. Warmuth. Learnability and the Vapnik-Chervonenkis Dimension. Journal of the Association for Computing Machinery, 36 (4): 929 - 965, 1989.
L. Dehaspe and L. De Raedt. Mining Association Rules in Multiple Relations. In Lavraˇc and Dˇzeroski [9], pages 125-132.
D. Gunopulos, H. Mannila, R. Khardon, and H. Toivonen. Data Mining, Hypergraph Transversals, and Machine Learning (extended abstract). In Proceedings of the 16th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, pages 209-216, Tucson, Arizona, USA, 1997. ACM Press.
D. Haussler. Quantifying inductive bias: AI learning algorithms and Valiant’s learning framework. Artificial Intelligence, 36:177–221, 1988.
D. Haussler, S. Ben-David, N. Cesa-Bianchi, and P. Long. Characterizations of Learnability for Classes of {0,…,n}-valued Functions. J. Comp. Sys. Sci., 50 (1): 74 - 86, 1995.
H. Hirsh. Incremental Version Space Merging: A General Framework for Concept Learning. Kluwer, 1990.
N. Lavraˇc and S. Dˇzeroski, editors. Inductive Logic Programming, 7th International Workshop, ILP-97, volume 1297 of Lecture Notes in Artificial Intelligence. Springer, 1997.
N. Lavraˇc, D. Gamberger, and V. Jovanoski. A Study of Relevance for Learning in Deductive Databases. Journal of Logic Programming, 40 (2/3): 215 - 249, 1999.
H. Mannila and H. Toivonen. Levelwise Search and Borders of Theories in Knowledge Discovery. Data Mining and Knowledge Discovery, 1 (3): 241 - 258, 1997.
F.A. Mârginean. Combinatorics of Fineness and Refinement. PhD thesis, Department of Computer Science, The University of York, 2001.
T.M. Mitchell. Version Spaces: An Approach to Concept Learning. PhD thesis, Electrical Engineering Department, Stanford University, 1979.
T.M. Mitchell. Machine Learning. McGraw-Hill, New York, 1997.
L. Pitt and L. G. Valiant. Computational limitations on learning from examples. Journal of the ACM, 35 (4): 965 - 984, 1988.
M. Sebag. Delaying the Choice of Bias: A Disjunctive Version Space Approach. In R. Bajcsy, editor. Proceedings of the 13th International Conference on Machine Learning, IJCAI 1993. Morgan-Kaufmann, August–September 1993.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mărginean, F.A. (2004). Soft Learning: A Conceptual Bridge between Data Mining and Machine Learning. In: Lotfi, A., Garibaldi, J.M. (eds) Applications and Science in Soft Computing. Advances in Soft Computing, vol 24. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45240-9_33
Download citation
DOI: https://doi.org/10.1007/978-3-540-45240-9_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40856-7
Online ISBN: 978-3-540-45240-9
eBook Packages: Springer Book Archive