Clusters of Genetic-based Attributes Selection of Cancer Data

Abstract

Clustering of data simplifies the task of data analysis and results in better disease diagnosis. Well-existing K-Means clustering hard computes clusters. Due to which the data may be centered to a specific cluster having less concentration on the effect of the coupling of clusters. Soft Computing methods are widely used in medical field as it contains fuzzy natured data. A Soft Computing approach of clustering called Fuzzy C-Means (FCM) deals with coupling. FCM clustering soft computes the clusters to determine the clusters based on the probability of having memberships in each of the clusters. The probability function used, determines the extent of coupling among the clusters. In order to achieve the computational efficiency and binding of features genetic evaluation is introduced. Genetic-based features are identified having more cohesion based on the fitness function values and then the coupling of the clusters is done using K-Means clustering in one trial and FCM in another trial. Analysis of coupling and cohesion is performed on Wisconsin Breast Cancer Dataset. Nature of clusters formations are observed with respect to coupling and cohesion.