Abstract
There is an increasing interest for multi-view clustering due to its ability to manage data from several sources. The majority of multi-view clustering algorithms are suitable to analyse vector data, but much less attention has been given for the analysis of relational data. This paper provides a fuzzy clustering algorithm with multi-medoids for multi-view relational data (MFMMdd). Experiments with real multi-view data sets show the good performance of the MFMMdd in comparison with previous multi-view clustering algorithms for relational data, concerning the quality of the partitions provided by these algorithms.
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 subscriptionsReferences
Yang, Y., Wang, H.: Multi-view clustering: a survey. Big Data Min. Anal. 1(2), 83–107 (2018)
Shenl, R., Olshen, A.B., Ladanyi, M.: Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer sub-type analysis. Bioinformatics 25, 2906–2912 (2009)
Cleuziou, G., Exbrayat, M., Martin, L., Sublemontier, J.-H.: CoFKM: a centralized method for multiple-view clustering. In: Ninth IEEE International Conference on Data Mining, pp. 752–757. IEEE Press, New York (2009)
Frigui, H., Hwang, C., Rhee, F.C.-H.: Clustering and aggregation of relational data with applications to image database categorization. Pattern Recogn. 40, 3053–3068 (2007)
Mei, J.-P., Chen, L.: Fuzzy relational clustering around medoids: a unified view. Fuzzy Sets Syst. 183, 44–56 (2011)
Gao, Y., Sun, C., Qi, H., Wang, S.: Fuzzy clustering based on weighted multi-medoids and multi-matrices. Int. J. Adv. Comput. Technol. 6, 61–70 (2014)
de Carvalho, F.A.T., Lechevallier, Y., de Melo, F.M.: Relational partitioning fuzzy clustering algorithms based on multiple dissimilarity matrices. Fuzzy Sets Syst. 215, 1–28 (2013)
Hathaway, R., Bezdek, J.: Nerf c-means: non-Euclidean relational fuzzy clustering. Pattern Recogn. 27, 429–437 (1994)
Krishnapuram, R., Joshi, A., Yi, L.: A fuzzy relative of the k-medoids algorithm with application to web document and snippet clustering. In: IEEE International Fuzzy Systems Conference, pp. 1281–1286 (1999)
D’Urso, P.: Dissimilarity measures for time trajectories. J. Ital. Stat. Soc. 1–3, 53–83 (2000)
Schwaemelle, V., Norregaard, O.: A simple and fast method to determine the parameters for fuzzy c-means cluster analysis. Bioinformatics 26, 2841–2848 (2010)
Huellermeier, E., Henzgen, S., Senge, R.: Comparing fuzzy partitions: a generalization of the rand index and related measures. IEEE Trans. Fuzzy Syst. 20, 546–556 (2012)
Manning, C.D., Raghavan, P., Schuetze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. Chapman and Hall/CRC, Boca Raton (1984)
Acknowledgments
The authors would like to thank the anonymous referees for their careful revision, and the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico - CNPq (303187/2013-1) for partially support this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Simões, E.C., de Carvalho, F.d.A.T. (2019). A Fuzzy Clustering Algorithm with Multi-medoids for Multi-view Relational Data. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11554. Springer, Cham. https://doi.org/10.1007/978-3-030-22796-8_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-22796-8_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22795-1
Online ISBN: 978-3-030-22796-8
eBook Packages: Computer ScienceComputer Science (R0)