Extracting Exact and Approximate Rules from Databases
Conference paper
Abstract
In addition to being a technique for classifying objects and defining concepts from data, the concept lattice may be exploited to discover functional dependencies as well as exact and approximate (probabilistic) implication rules between properties (descriptors). This paper presents algorithms for rule generation and shows that the lattice is an interesting framework for that purpose.
Keywords
Concept Lattice Inductive Logic Programming Formal Concept Analysis Concept Hierarchy Inductive Learning
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
- [1]Agrawal R, Imielinski T and Swami A. Mining association rules between sets of items in large databases. Proc. ACM SIGMOD’93 Conf., 1993 pp 207–216Google Scholar
- [2]Cai Y, Cercone N and Han J. Attribute-oriented induction in relational databases. In: Piatetsky-Shapiro G and Frawley WJ (eds) Knowledge Discovery from Databases. Menlo Park, CA, AAAI Press/The MIT Press, 1991 pp 213–228Google Scholar
- [3]Cercone N. and Tsuchiya M (eds). Special issue on learning and discovery in knowledge-based databases. In: Knowledge and Data Engineering, 5(6), 1993Google Scholar
- [4]Davey BA and Priestley HA. Introduction to Lattices and Order. Cambridge University Press, Cambridge, 1990, p 248MATHGoogle Scholar
- [5]Gennari JH, Langley P and Fisher D. Models of incremental concept formation. In: Carbonell J (ed) Machine learning: paradigms and methods. MIT Press, Amsterdam, The Netherlands, 1990, pp 11–62Google Scholar
- [6]Gale WA (ed). Artificial Intelligence and Statistics. Addison-Wesley, Reading, Menlo Park, Don Mills, 1986Google Scholar
- [7]Godin R, Mineau G and Missaoui R. Rapport de la phase 2 du projet Macroscope pour le volet Réutilisation, 1993Google Scholar
- [8]Godin R, Missaoui R and Alaoui H. Incremental concept formation algorithms based on Galois (concept) lattices. Technical Report, Département de Mathématiques et d’Informatique, Université du Québec à Montréal, 1994. Also submitted for publication.Google Scholar
- [9]Godin R and Missaoui R. An incremental concept formation approach for learning from databases. Technical Report, Département de Mathématiques et d’Informatique, Université du Québec à Montréal, 1994. Also submitted for publication.Google Scholar
- [10]Holsheimer M and Siebes A. Data mining: the search for knowledge in databases. Technical Report CS-R9406, 1993Google Scholar
- [11]Ioannidis YE, Saulys T and Whitsitt AJ. Conceptual learning in database design. ACM Trans. on Information Systems, 10 (3), 1992, pp 265–293CrossRefGoogle Scholar
- [12]Kaufman KA, Michalski RS and Kerschberg L. Mining for knowledge in databases: goals and general description of the INLEN system. In: Piatetsky-Shapiro G and Frawley WJ (ed) Knowledge Discovery from Databases, AAAI Press/The MIT Press, Menlo Park, CA, 1991, pp 449–462Google Scholar
- [13]Kivinen J and Mannila H. Approximate dependency inference from relations. In: Biskup J and Hull R (ed) 4th Int. Conf. on Database Theory, Springer-Verlag, London, 1992, pp 86–98Google Scholar
- [14]Michalski RS and Kodratoff Y. Research in machine learning: recent progress, classification of methods, and future directions. In: Kodratoff Y and Michalski RS (ed) Machine Learning: An Artificial Intelligence Approach, Morgan Kaufmann, San Mateo, CA, 1990, pp 1–30Google Scholar
- [15]Maier D. The theory of Relational Databases, Computer Science Press, Rockville, Md, 1983Google Scholar
- [16]Mineau G and Godin R. Automatic structuring of knowledge bases by conceptual clustering. IEEE Trans. on Knowledge and Data Engineering, accepted for publicationGoogle Scholar
- [17]Minker J (ed). Foundations of Deductive Databases and Logic Programming. Morgan Kaufmann, Los Altos, CA, 1988Google Scholar
- [18]Missaoui R and Godin R. An incremental concept formation approach for learning from databases. In: Alagar VS, Lakshmanan VS and Sadri F (ed) Workshop on Formal Methods in Databases and Software Engineering. Montreal, May 15–16, 1992. Springer-Verlag, London, 1993, pp 39–53CrossRefGoogle Scholar
- [19]Missaoui R and Godin R. Search for concepts and dependencies in databases. In: Ziarko W (ed) International Workshop on Rough Sets and Knowledge Discovery. Banff, October 1993. Springer-Verlag, London, 1994, to appearGoogle Scholar
- [20]Muggleton S, De Raedt L. Inductive logic programming: theory and methods. Submitted to The Journal of Logic Programming, 1993Google Scholar
- [21]Pawlak Z. Rough sets: theoretical aspects of reasoning about data. Kluwer Academic, Dordrecht, Boston, London, 1992Google Scholar
- [22]Piatetsky-Shapiro G and Frawley WJ (ed). Knowledge discovery in databases AAAI Press/The MIT Press, Menlo Park, CA, 1991, p 525Google Scholar
- [23]Piatetsky-Shapiro G. Discovery, analysis, and presentation of strong rules. In: Piatetsky-Shapiro G and Frawley WJ (eds) Knowledge Discovery in Databases. AAAI Press/The MIT Press, Menlo Park, CA, 1991, pp 229–248Google Scholar
- [24]Slowinski R (ed). Lntelligent decision support. In: Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic, Dordrecht, Boston, London, 1992Google Scholar
- [25]Smyth P and Goodman RM. Rule induction using information theory. In: Piatetsky-Shapiro G and Frawley WJ (eds) Knowledge Discovery in Databases. AAAI Press/The MIT Press, Menlo Park, CA, 1991, pp 159–167Google Scholar
- [26]Wille R. Knowledge acquisition by methods of formal concept analysis. In: Diday E (ed) Data Analysis, Learning Symbolic and Numeric Knowledge. Nova Science, New York, 1989, pp 365–380Google Scholar
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© British Computer Society 1994