Abstract
In the data mining field many clustering methods have been proposed, yet standard versions do not take into account uncertain databases. This paper deals with a new approach to cluster uncertain data by using a hierarchical clustering defined within the belief function framework. The main objective of the belief hierarchical clustering is to allow an object to belong to one or several clusters. To each belonging, a degree of belief is associated, and clusters are combined based on the pignistic properties. Experiments with real uncertain data show that our proposed method can be considered as a propitious tool.
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
Bezdek, J.C., Ehrlich, R., Fulls, W.: Fcm: The fuzzy c-means clustering algorithm. Computers and Geosciences 10(2-3), 191–203 (1984)
Denœux, T.: A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 25(5), 804–813 (1995)
Denœux, T., Masson, M.: EVCLUS: Evidential Clustering of Proximity Data. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 34(1), 95–109 (2004)
Ben Hariz, S., Elouedi, Z., Mellouli, K.: Clustering approach using belief function theory. In: AIMSA, pp. 162–171 (2006)
Hastie, T., Tibshirani, R., Friedman, J., Franklin, J.: The elements of statistical learning; data mining, inference and prediction. Springer, New York (2001)
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 11 (1967)
Masson, M., Denœux, T.: Clustering interval-valued proximity data using belief functions. Pattern Recognition Letters 25, 163–171 (2004)
Schubert, J.: Clustering belief functions based on attracting and conflicting metalevel evidence. In: Bouchon-Meunier, B., Foulloy, L., Yager, R.R. (eds.) Intelligent Systems for Information Processing: From Representation to Applications, Elsevier, Amsterdam (2003)
Shafer, G.: Mathematical Theory of evidence. Princeton Univ. (1976)
Smets, P., Kennes, R.: The Transferable Belief Model. Artificial Intelligent 66, 191–234 (1994)
Zouhal, L.M., Denœux, T.: An Evidence-Theoric k-NN Rule with Parameter Optimization. IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews 28(2), 263–271 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Maalel, W., Zhou, K., Martin, A., Elouedi, Z. (2014). Belief Hierarchical Clustering. In: Cuzzolin, F. (eds) Belief Functions: Theory and Applications. BELIEF 2014. Lecture Notes in Computer Science(), vol 8764. Springer, Cham. https://doi.org/10.1007/978-3-319-11191-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-11191-9_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11190-2
Online ISBN: 978-3-319-11191-9
eBook Packages: Computer ScienceComputer Science (R0)