UM
Multi-attribute based fuzzy C-means in approximated feature space
Liu Z.1; Chen C.L.P.1; Chen L.1; Zhou J.4
2018-03-09
Source Publication2017 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2017
Volume2017-November
Pages1-6
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
DOI10.1109/iFUZZY.2017.8311818
URLView the original
Language英語
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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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