Abstract
Uncertain relationship lies in data between the data sets as well as within a data set. Practically, data of the same group of objects are usually stored in different data sets; in each data set, the data dimensions are not necessarily the same and unreal data may exist. Fuzzy clustering of a single data set would bring about less reliable results. And these data sets can’t be integrated for some reasons.
In this paper, the method of first fuzzy clustering of single data sets and then optimizing in accordance with the dependency of these data sets is adopted so as to improve the quality of fuzzy clustering of a single data set with the help of other data sets, taking confidentiality and security of the data into consideration.
The method of fuzzy clustering with collaboration between multi data sets meets application demands on special occasions, attaching to the connection of the data sets and detached from the source data.
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
Gao, X.-B., Pei, J.-H., Xie, W.-X.: A Study of Weighting Exponent m in a Fuzzy c-Means Algorithm. Chinese Journal of Electronics 28(4), 80–83 (2000)
Qi, H.-Y., Wu, X.-J., Wang, S.-T., Yang, J.-Y.: Collaborative FCPM Fuzzy Clustering Algorithm. Pattern Recognition and Artificial In-telligence 23(01), 120–126 (2010)
Qi, H.-Y., Wu, X.-J., Wang, S.-T., Yang, J.-Y.: Fuzzy modeling based on feature selection and collaborative fuzzy clustering. Computer Engineering and Applications 44(19), 46–49 (2008)
Qi, H.-Y.: Improvement Research of Fuzzy Model Algorithm. Jiangnan University, Wuxi, Jiangsu Province, China (2008)
Pedrycz, W.: Collaborative Fuzzy Clustering. Pattern Recognition Letters 23(14), 1675–1686 (2002)
Abonyi, J., Roubos, J.A.: Compact TS-fuzzy models through clustering and OLS plus FIS model reduction. In: IEEE International Conference on Fuzzy Systems, Sydney, Australia (2001)
Abonyi, J., Babuska, B., Szeifert, F.: Modified gath-geva fuzzy clustering for identi-fication of Takagi-Sugeno fuzzy models. IEEE Transactions on Systems, Man, and Cybernetics 32(5), 612–621 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Zhong, Zs., Wang, G., Huang, Yq. (2012). Research into Fuzzy Clustering with Collaboration between Multi Data Sets. In: Qian, Z., Cao, L., Su, W., Wang, T., Yang, H. (eds) Recent Advances in Computer Science and Information Engineering. Lecture Notes in Electrical Engineering, vol 124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25781-0_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-25781-0_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25780-3
Online ISBN: 978-3-642-25781-0
eBook Packages: EngineeringEngineering (R0)