Abstract
Automatic classification may be used in object knowledge bases in order to suggest hypothesis about the structure of the available object sets. Yet its direct application meets some difficulties due to the way data is represented: attributes relating objects, multi-valued attributes, non-standard and external data types used in object descriptions. We present here an approach to the automatic classification of objects based on a specific dissimilarity model. The topological measure, presented in a previous paper, accounts for both object relations and the variety of available data types. In this paper, the extension of the topological measure on multi-valued object attributes, e.g. lists or sets, is presented. The resulting dissimilarity is completely integrated in the knowledge model Tropes which enables the definition of a classification strategy for an arbitrary knowledge base built on top of Tropes.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
R.K. Ahuja, T.L. Magnanti, and J.B. Orlin, Network Flows: Theory, Algorithms and Applications, Prentice Hall, 1993.
G. Bisson, ‘Conceptual clustering in a first order logic representation', in Proceedings of the 10th European Conference on Artificial Intelligence, Vienna, Austria, pp. 458–462, (1992).
G. Bisson, ‘Why and how to define a similarity measure for object-based representation systems', in Towards Very Large Knowledge Bases, ed., N.J.I. Mars, pp. 236–246, Amsterdam, (1995). IOS Press.
C. Capponi, Identification et exploitation des types dans un modèle de connaissances à objets, Ph.D. dissertation, Joseph Fourier, Grenoble (FR), 1995.
C. Capponi, J. Euzenat, and J. Gensel, ‘Objects, types and constraints as classification schemes', in Proceedings of the 1st KRUSE symposium, pp. 69–73, Santa Cruz (CA US), (1995).
P. Cheeseman, J. Kelly, M. Self, J. Stutz, W. Taylor, and D. Freeman, ‘Autoclass: A bayesian classification system', in Proceedings of the 5th Internatinal Conference on Machine Learning, Ann Arbor, MI, pp. 54–56, (1988).
W. Cohen, ‘Learning trees and rules with set-valued features', in Proceedings of the 13th AAAI and 8th IAAI, (1996).
F. Esposito, ‘Conceptual clustering in structured domains: a theory guided approach', in New Approaches in Classification and Data Analysis, eds., E. Diday, Y. Lechevallier, M. Schader, P. Bertrand, and B. Burtschy, pp. 395–404, Berlin, (1994). Springer Verlag.
J. Euzenat, ‘Brief overview of t-tree: the Tropes taxonomy building tool', in Proceedings of the 4th ASIS SIG/CR classification research workshop, pp. 69–87, Columbus (OH US), (1993).
D.H. Fisher, ‘Knowledge acquisition via incremental conceptual clustering', Machine Learning, 2, 139–172, (1987).
R. Godin, G.W. Mineau, and R. Missaoui, ‘Incremental structuring of knowledge bases', in Proceedings of the 1st KRUSE symposium, pp. 179–193, Santa Cruz (CA US), (1995).
A. Ketterlin, P GanÇarski, and J.J. Korczak, ‘Hierarchical clustering of composite objects with variable number of components', in Proceedings of the 5th International Workshop on Artificial Intelligence and Statistics, eds., D. H. Fisher and P. Lenz, Fort Lauerdale (FL USA), (1995).
R. Michalski and R. Stepp, Machine learning: an Artificial Intelligence approach, volume I, chapter Learning from observation: conceptual clustering, 331–363, Tioga publishing company, Palo Alto (CA US), 1983.
G Piatetsky-Shapiro and W. Frawley, Knowledge discovery in databases, AAAI Press, 1991.
Sherpa project, Tropes 1.0 reference manual, INRIA RhÔne-Alpes, Grenoble (FR), 1995.
R. Rada, H. Mili, E. Bicknell, and M. Blettner, ‘Development and application of a metric on semantic nets', IEEE Transactions on Systems, Man and Cybernetics, 19(1), 17–30, (1989).
K. Thompson and P. Langley, Knowledge and experience in unsupervised learning, chapter Concept formation in structured domains, 127–161, Morgan Kaufman, San Mateo (CA US), 1991.
P. Valtchev and J. Euzenat, ‘Classification of concepts through products of concepts and abstract data types', in Ordinal and symbolic data analysis, eds., Y. Lechevallier E. Diday and O. Opitz, pp. 3–12, Heildelberg (DE), (1996). Springer Verlag.
B. van Cutsem, Classification and dissimilarity analysis, Lecture notes in statistics, Springer Verlag, New York, 1994.
D. Ziébelin, A. Vila, and V. Rialle, ‘Neuromyosys a diagnosis knowledge based system for emg', in Proceedings of the 12th International Congress of Medical Informatics in Europe, Lisboa (PT), (1994).
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag
About this paper
Cite this paper
Valtchev, P., Euzenat, J. (1997). Dissimilarity measure for collections of objects and values. In: Liu, X., Cohen, P., Berthold, M. (eds) Advances in Intelligent Data Analysis Reasoning about Data. IDA 1997. Lecture Notes in Computer Science, vol 1280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0052846
Download citation
DOI: https://doi.org/10.1007/BFb0052846
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63346-4
Online ISBN: 978-3-540-69520-2
eBook Packages: Springer Book Archive