Multi-attribute based fuzzy C-means in approximated feature space
Liu Z.1; Chen C.L.P.1; Chen L.1; Zhou J.4
Source Publication2017 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2017
AbstractRecently, clustering illustrates its importance in knowledge discovery. However, most of the considered algorithms are efficient only on those linear separable datasets. Although the kernel based methods perform better with non-linear separable ones, at the same time, they are suffered from the missing of priority knowledge of a suitable kernel. Two latest methods which approximate date to infinity dimensional kernel feature spaces are considered in this paper. Furthermore, new attribute multi-kernel weighted fuzzy c-means are proposed by combining the randomly projected features. From the experiments shown in the simulation section, we conclude that we develop an improved version of attribute multi-kernel weighted fuzzy c-means, which achieves better accuracy.
KeywordFuzzy C-means Maximum Entropy Method for weighted data Quasi-Monte Carlo Feature maps Random Features Random Fourier Features maps Weighted Multi-kernel Fuzzy C-means
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Universidade de Macau
2.Dalian Maritime University
3.Institute of Automation Chinese Academy of Sciences
4.University of Jinan
Recommended Citation
GB/T 7714
Liu Z.,Chen C.L.P.,Chen L.,et al. Multi-attribute based fuzzy C-means in approximated feature space[C],2018:1-6.
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