Abstract
Collaborative utilization of multiple cooccurrence information is expected a powerful tool for knowledge discovery in many real applications. This chapter presents a brief review on three-mode fuzzy co-clustering, which reveals the intrinsic co-cluster structures from three-mode cooccurrence information. Additionally, a secure process of collaborative analysis among different organizations is also considered with illustrative examples.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Oh, C.-H., Honda, K., Ichihashi, H, Fuzzy clustering for categorical multivariate data, Proc. of Joint 9th IFSA World Congress and 20th NAFIPS International Conference, 2154–2159 (2001)
J.C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms (Plenum Press, 1981)
Honda, K., Suzuki, Y., Ubukata, S., Notsu, A.: FCM-type fuzzy coclustering for three-mode cooccurrence data: 3FCCM and 3Fuzzy CoDoK, Advances in Fuzzy Systems, 2017, #9842127, 1–8 (2017)
C.C. Aggarwal, P.S. Yu, Privacy-Preserving Data Mining: Models and Algorithms (Springer-Verlag, New York, 2008)
Vaidya, J., Clifton, C.: Privacy-preserving \(K\)-means clustering over vertically partitioned data, Proc. of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 206–215, Washington, DC, USA (2003)
Jha, S., Kruger, L., Mcdaniel.: Privacy preserving clustering, Proc. of the 10th European Symposium On Research In Computer Security, 397–417 (2005)
Samet, S., Miri, A., Orozco-Barbosa, L.: Privacy preserving \(k\)-means clustering in multi-party environment. Proc. of the International Conference on Security and Cryptography, 381–385 (2007)
A. İnan, S.V. Kaya, Y. Saygın, E. Savaş, A.A. Hintoğlu, A. Levi, Privacy preserving clustering on horizontally partitioned data. Data & Knowledge Engineering 63, 646–666 (2007)
Yu, T.-K., Lee, D.T., Chang, S.-M., Zhan, J.: Multi-party \(k\)-means clustering with privacy consideration, Proc. of the International Symposium on Parallel and Distributed Processing with Applications, 200–207 (2010)
Honda, K., Oda, T., Tanaka, D., Notsu, A.: A collaborative framework for privacy preserving fuzzy co-clustering of vertically distributed cooccurrence matrices, Advances in Fuzzy Systems, 2015, #729072, 1–8 (2015)
Honda, K., Matsuzaki, S., Ubukata, S., Notsu, A.: Privacy preserving collaborative fuzzy co-clustering of three-mode cooccurrence data, Proc. of 15th International Conference on Modeling Decisions for Artificial Intelligence, LNAI-11144, 232–242, Springer (2018)
Miyamoto, S., Mukaidono, M.: Fuzzy \(c\)-means as a regularization and maximum entropy approach, Proc. of the 7th International Fuzzy Systems Association World Congress, 2, 86–92 (1997)
S. Miyamoto, H. Ichihashi, K. Honda, Algorithms for Fuzzy Clustering (Springer, 2008)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Chen, TC.T., Honda, K. (2020). Three-Mode Fuzzy Co-clustering and Collaborative Framework. In: Fuzzy Collaborative Forecasting and Clustering. SpringerBriefs in Applied Sciences and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-22574-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-22574-2_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22573-5
Online ISBN: 978-3-030-22574-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)