An Intelligent Classifier for Breast Cancer Diagnosis based on K-Means Clustering and Rough Set

Abstract

Feature selection aims to select subset of original features. It removes unrelated, redundant or noisy data from the problem domain. Rough set theory is often applied to feature reduction using the data alone, requiring no additional information and widely used for classification tool in data mining. Clustering, a form of data grouping, groups a set of data such that the intra-cluster similarity is maximized and the inter-cluster similarity is minimized. In this paper, k-means clustering algorithm is applied to partition the given information system and further rough set theory implemented on the data set to generate feature subset. The classification process by means of SVM is performed by using the remaining features. Wisconsin Breast Cancer datasets derived from UCI machine learning database are used for the purpose of testing the proposed hybrid model and the success rate of hybrid model is determined as 99%. General Terms Pattern Recognition, Machine learning.