Abstract
This chapter has two goals. The first goal is to compare Machine Learning (ML) and Knowledge Discovery in Data (KDD, also often called Data Mining, DM) insisting on how much they actually differ. In order to make my ideas somewhat easier to understand, and as an illustration, I will include a description of several research topics that I find relevant to KDD and to KDD only. The second goal is to show that the definition I give of KDD can be almost directly applied to text analysis, and that will lead us to a very restrictive definition of Knowledge Discovery in Texts (KDT). I will provide a compelling example of a real-life set of rules obtained by what I call KDT techniques.
This is a preview of subscription content, log in via an institution to check access.
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
Bhandari, I. “Attribute focusing: Machine-Assisted Knowledge Discovery Applied to Software Production Process Control”, Knowledge Acquisition 6, 271–294, 1994.
Brin S., Motwani R., Ullman J. D., Tsur S., “Dynamic itemset Counting and Implication Rules for Market Basket Data,” Proc. ACM SIGMOD International Conference on Management of Data, pp. 255–264, 1997.
Fayyad U. M., Piatetsky-Shapiro G., Smyth P., “From Data Mining to Knowledge Discovery: An Overview,” in Fayyad U. M., Piatetsky-Shapiro G., Smyth P, Uthurasamy R. (Eds.), Advances in Knowledge Discovery and Data mining, AAAI Press, 1996.
Gago P., Bento C., “A Metric for Selection of the Most Promising Rules,” in Principles of Data Mining and Knowledge Discovery, Zytkow J. & Quafafou M. (Eds.), pp. 19–27, LNAI 1510, Springer, Berlin 1998.
Gras R., Lahrer A., #x201C;L#x2019;implication statistique: une nouvelle m#x00E9;thode d#x2019;analyse des donn#x00E9;es,#x201D; Math#x00E9;matiques Informatique et Sciences Humaines 120:5–31, 1993.
Kodratoff Y, Bisson G. “The epistemology of conceptual clustering: KBG, an implementation”, Journal of Intelligent Information Systems, 1:57–84, 1992.
Kodratoff Y., “Induction and the Organization of Knowledge”, Machine Learning: A Multistrategy Approach, volume 4, Tecuci G. et Michalski R. S. (Eds.), pages 85–106. Morgan-Kaufmann, San Francisco CA, 1994.
Kodratoff Y., “Knowledge Discovery in Texts: A Definition, and Applications,#x201D; Proc. ISMIS#x2019;99, Warsaw, June 1999. Published in Foundation of Intelligent Systems, Ras & Skowron (Eds.) LNAI 1609, pp. 16–29, Springer 1999
Partridge D., “The Case for Inductive Programming,” IEEE Computer 30,1, 36–41, 1997. A more complete version in: “The Case for Inductive Computing Science,” in Computational Intelligence and Software Engineering, Pedrycz & Peters (Eds.) World Scientific, in press.
Pearl J., Verma T. S., #x201C;A Theory of Inferred Causation,#x201D; Proc. 2nd International Conf. on Principles of Knowledge Representation and Reasoning, pp. 441–452, 1991.
Searle J. R. Minds, brains & science, Penguin books, London 1984.
Searle J. R., Scientific American n#x00B0;262, 1990, pp. 26–31.
Sebag M., “2nd order Understandability of Disjunctive Version Spaces,#x201D; Workshop on Machine Learning and Comprehensibility organized at IJCAI-95, LRI Report, Universite Paris-Sud.
Sebag M., “Delaying the choice of bias: A disjunctive version space approach,#x201D; Proc. 13th International Conference on Machine Learning, Saitta L. (Ed.), pp. 444–452, Morgan Kaufmann, CA 1996.
Suzuki E. “Autonomous Discovery of Reliable Exception Rules,#x201D; Proc. KDD-97, 259–262, 1997.
Suzuki E., Kodratoff Y., #x201C;Discovery of Surprising Exception Rules Based on Intensity of Implication,”, in Principles of Data Mining and Knowledge Discovery, Zytkow J. & Quafafou M. (Eds.), pp. 10–18, LNAI 1510, Springer, Berlin 1998.
Suppes P., “A Probalistic Theory of Causality,#x201D; Acta Philosophica Fennica, Fasc. XXIV, 1970.
Think, June 1993, a review published by ITK, Univ. Tilburg, Warandelaan 2, PO Box 90153, 5000 Le Tilburg, The Netherlands.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kodratoff, Y. (2001). Comparing Machine Learning and Knowledge Discovery in DataBases: An Application to Knowledge Discovery in Texts. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds) Machine Learning and Its Applications. ACAI 1999. Lecture Notes in Computer Science(), vol 2049. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44673-7_1
Download citation
DOI: https://doi.org/10.1007/3-540-44673-7_1
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42490-1
Online ISBN: 978-3-540-44673-6
eBook Packages: Springer Book Archive