Effective Kernel-Based Fuzzy Clustering Systems in Analyzing Cancer Database

The greatest challenge in high-dimensional medical cancer databases is to differentiate the available subtypes due to uncertainty associated with the objects of the database. Recently, mathematical algorithm-based diagnosing system plays an increasingly important role in analyzing the high-dimensional medical cancer databases. Particularly, fuzzy clustering technique has taken a major role in clustering medical cancer databases. The fuzzy clustering techniques are not robust with high-dimensional databases which have more similar objects. Therefore, this paper tries to propose robust fuzzy clustering algorithms for effective analysis of high-dimensional medical database. This paper introduces the objective function of proposed robust fuzzy clustering techniques by incorporating Laplacian kernel-induced distance, Canberra distance, possibilistic memberships, and fuzzy memberships. The proposed methods have been implemented with high-dimensional breast cancer database containing three subclasses which are the leading causes of cancer deaths in the world. Benchmark datasets have been used to evaluate the performance of the proposed methods, and this paper has shown the effectiveness of the proposed methods through clustering accuracy.

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