Transfer fuzzy c-means clustering algorithm with center distance maximization
Author(s): LI Ming, XIA Hong-bin, School of Digital Media, Jiangnan University
Pages: 2206-2212
Year: 2016 Issue: 8
Journal: Computer Engineering and Design
Keyword: clustering algorithm; historical knowledge; transfer learning; privacy protection; center distance maximization;
Abstract: To address these issues that traditional clustering algorithms do not work well,and even are prone to fail when the data are quite sparse or distorted due to plenty of noise or outliers,the transfer fuzzy c-means clustering algorithm with center distance maximization(CMT-FCM),which benefited from the guidance of historical knowledge,was proposed.It was verified to be highly effective,and the privacy of raw data was protected.In the situations where the data are distorted due to much noise,interference information appeals to every class center to some extent,leading to shift or consistency of class center.Using the algorithm avoided the problem by introducing center distance maximization.Experimental studies on both artificial and real-world datasets demonstrate the effectiveness of the algorithm.
Pages: 2206-2212
Year: 2016 Issue: 8
Journal: Computer Engineering and Design
Keyword: clustering algorithm; historical knowledge; transfer learning; privacy protection; center distance maximization;
Abstract: To address these issues that traditional clustering algorithms do not work well,and even are prone to fail when the data are quite sparse or distorted due to plenty of noise or outliers,the transfer fuzzy c-means clustering algorithm with center distance maximization(CMT-FCM),which benefited from the guidance of historical knowledge,was proposed.It was verified to be highly effective,and the privacy of raw data was protected.In the situations where the data are distorted due to much noise,interference information appeals to every class center to some extent,leading to shift or consistency of class center.Using the algorithm avoided the problem by introducing center distance maximization.Experimental studies on both artificial and real-world datasets demonstrate the effectiveness of the algorithm.
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