Abstract
A new clustering method, named Evidential clustering by Competitive Agglomeration (ECA), is introduced by applying the framework of belief functions to a competitive strategy. It has two-fold advantages: Firstly, with the help of the credal partition, it has a good ability to deal with noise objects since it can mine the ambiguity and uncertainty of the data structure; secondly, through a competitive strategy, it can automatically gain the number of clusters under the rule of intra-class compactness and inter-class dispersion. Results demonstrate the effectiveness of the proposed method on synthetic and real-world datasets.
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
References
Gong, C., Su, Z.G., Wang, P.H., Wang, Q.: An evidential clustering algorithm by finding belief-peaks and disjoint neighborhoods. Pattern Recogn. 113, 2 (2020)
Gong, C.Y., Su, Z.G., Wang, P.H., You, Y.: Distributed evidential clustering toward time series with big data issue. Expert Syst. Appl. 191, 116279 (2022)
Gong, C.Y., Wang, P.H., Su, Z.G.: An interactive nonparametric evidential regression algorithm with instance selection. Soft. Comput. 24(5), 3125–3140 (2020)
Gong, C.Y., Su, Z.G., Wang, P.H., Wang, Q., You, Y.: Evidential instance selection for k-nearest neighbor classification of big data. Int. J. Approximate Reasoning 138, 123–144 (2021)
Gong, C., Su, Z.G., Wang, P.H., Wang, Q., You, Y.: A sparse reconstructive evidential-nearest neighbor classifier for high-dimensional data. IEEE Trans. Knowl. Data Eng. (2022)
Han, J., Kamber, M., Pei, J.: EnglishData mining: concepts and techniques (2012)
Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Kluwer academic (1981)
Frigui, H., Krishnapuram, R.: Clustering by competitive agglomeration. Pattern Recogn. 30(7), 1109–1119 (1997)
Grira, N., Crucianu, M., Boujemaa, N.: Semi-supervised fuzzy clustering with pairwise-constrained competitive agglomeration. In: Fuzzy Systems, 2005. FUZZ 2005. The 14th IEEE International Conference on (2005)
Grira, N., Crucianu, M., Boujemaa, N.: Active semi-supervised fuzzy clustering. Pattern Recogn. 41(5), 1834–1844 (2008)
Gao, C.F., Wu, X.J.: A new semi-supervised clustering algorithm with pairwise constraints by competitive agglomeration. Appl. Soft Comput. J. 11(8), 5281–5291 (2011)
Krishnapuram, R., Keller, J.: A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst. 1(2), 98–110 (1993)
Shafer, G.: A Mathematical Theory of Evidence, vol. 1 (1976)
Masson, M.H., Denoeux, T.: ECM: an evidential version of the fuzzy c -means algorithm. Pattern Recogn. 41(4), 1384–1397 (2008)
Su, Z.G., Zhou, H.Y., Wang, P.H., Zhao, G., Zhao, M.: E2cm: an evolutionary version of evidential c-means clustering algorithm. In: Destercke, S., Denoeux, T., Cuzzolin, F., Martin, A. (eds.) Belief Functions: Theory and Applications, pp. 234–242. Springer International Publishing, Cham (2018). https://doi.org/10.1007/978-3-642-29461-7
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)
Su, Z.G., Denoeux, T.: BPEC: Belief-peaks evidential clustering. IEEE Trans. Fuzzy Syst. 27(1), 111–123 (2018)
Gong, C.Y., Su, Z.G., Wang, P.H., Wang, Q.: Cumulative belief peaks evidential k-nearest neighbor clustering. Knowl.-Based Syst. 200, 105982 (2020)
Bache, K., Lichman, M.: UCI machine learning repository (2013)
Chang, H., Yeung, D.-Y.: Robust path-based spectral clustering. Pattern Recogn. 41(1), 191–203 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, L., Wang, Q., Wang, Ph., Su, Zg. (2022). Evidential Clustering by Competitive Agglomeration. In: Le Hégarat-Mascle, S., Bloch, I., Aldea, E. (eds) Belief Functions: Theory and Applications. BELIEF 2022. Lecture Notes in Computer Science(), vol 13506. Springer, Cham. https://doi.org/10.1007/978-3-031-17801-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-17801-6_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-17800-9
Online ISBN: 978-3-031-17801-6
eBook Packages: Computer ScienceComputer Science (R0)