Abstract
A novel kernelized fuzzy attribute C-means clustering algorithm is proposed in this paper. Since attribute means clustering algorithm is an extension of fuzzy C-means algorithm with weighting exponent m = 2 , and fuzzy attribute C-means clustering is a general type of attribute C-means clustering with weighting exponent m > 1 , we modify the distance in fuzzy attribute C-means clustering algorithm with kernel-induced distance, and obtain kernelized fuzzy attribute C-means clustering algorithm. Kernelized fuzzy attribute C-means clustering algorithm is a natural generalization of kernelized fuzzy C-means algorithm with stable function. Experimental results on standard Iris database and tumor/normal gene chip expression data demonstrate that kernelized fuzzy attribute C-means clustering algorithm with Gaussian radial basis kernel function and Cauchy stable function is more effective and robust than fuzzy C-means, fuzzy attribute C-means clustering and kernelized fuzzy C-means as well.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
