Effective kernel-based possibilistic fuzzy clustering techniques: analyzing cancer database

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.

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