Abstract
We propose a heuristic method to mine type scheme semiautomatically from initial database scheme and the instances. Unlike conventional database design methods, the proposed one starts from examining database entities.
We assume one entity may have more than one types and classification (or type scheme) might be appropriate when each entity is classified into ast most k (least general) classes with respect to ISA hierarchy. Clearly, from the view point of database technique, it is suitable for each entity to keep limited number of type informations.
Our method differs from others in evolving ISA hierarchy by introducing semantical metric. We propose a sophisticated algorithm to evolve type schemes.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Ghosh, S. et al.: An Interval Classifier for Database Mining Applications, proc. VLDB (1992), 560–573
Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules, proc.VLDB (1994), 487–499
Cai, Y., Cercone, N. and Han, J.: An Attribute-Oriented Approach for Learning Classification Rules from Relational Databases, proc.ICDE (1990), 281–288
Cai, Y., Cercone, N. and Han, J.: An Attribute-Oriented Induction in Relational Databases, in [18], 213–228
Elmasri, R. and Navathe, S.: Fundamentals of Database Systems, Benjamin, 1989
Frawley, W.J., Piatetsky-Shapiro, G. and Matheus, C.J.: Knowledge Discovery in Databases — An Overview, in [18], 1–30
Han, J., Cai, Y. and Cercone, N.: Knowledge Discovery in Databases: An Attribute-Oriented Approach, proc. VLDB (1992), 547–559
Ioannidis, Y. and Lashkari, Y.: Incomplete Path Expressions and their Disambiguation, proc.SIGMOD (1994), 138–149
Michalski, R. et al.: Machine Learning (Vol. 1–4), Morgan Kauffman
Mineau,G.,Gecsei, J. and Godin, R.: Structuring Knowledge Bases Using Automatic Learning, proc.ICDE (1990), 274–280
Miura, T. and Ariswa, H.: Logic Approach of Data Models, proc.Future Databases (1990), 145–159
Miura, T.: Database Paradigms towards Model Buildings, proc.Object Role Modelling (1994)
Miura, T. and Shioya, I.: Strategy Control for Database Queries, proc.DEXA (1995), 231–240
Ng, R. and Han, J.: Efficient and Effective Clustering Methods for Spacial Data Mining, proc. VLDB (1994), 144–155
Ohsuga, S.: How can knowledge-based systems solve large-scale problems ?: Model-based decomposition and problem solving, Knowledge-Based Systems 6-1 (1993), 38–62
Quinlan, R.: Induction of Decision Trees, Machine Learning 1-1 (1986), 81–106
Quinlan, R.: Learning Logical Definition from Rules, Machine Learning 5-3 (1990), 239–266
Piatetsky-Shapiro, G. and Frawley, W.J. (ed.): Knowledge Discovery in Databases, MIT Press (1991)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1996 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Miura, T., Shioya, I. (1996). Mining type schemes in databases. In: Wagner, R.R., Thoma, H. (eds) Database and Expert Systems Applications. DEXA 1996. Lecture Notes in Computer Science, vol 1134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0034695
Download citation
DOI: https://doi.org/10.1007/BFb0034695
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-61656-6
Online ISBN: 978-3-540-70651-9
eBook Packages: Springer Book Archive