Abstract
This paper aims to present optimal clustering techniques for analyzing high-dimensional cancer databases with missing attributes and overlapped objects. Analyzing the high-dimensional database with missing values is considered as most difficult task, and so far, there is no optimal cluster technique available for clustering the cancer database. Therefore, this paper develops the effective fuzzy clustering techniques that incorporate Cauchy kernel induced distance, rudimentary centroids, possibilistic memberships, fuzzy memberships, and prototype equation. To reduce the computing time of algorithms, this paper introduces a method for finding reasonable initial cluster centers. Experimental results indicate that the proposed methods are suitable for the breast cancer databases with missing attributes, and the results indicate that the methods outperform in clustering the databases into available subclasses.
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.
