Publication

Abstract : Breast cancer is taking a large toll in the present scenario. Many computer aided diagnosis are been developed to detect breast cancer. The detected breast cancer is also classified according to their subtypes. In the absence of a class definition, analyzing the cancer types is huge some task. Clustering the breast cancer data is a process that merges the feature selection process and the process of defining the class labels for the data. The proposed work has four stages which include preprocessing, feature selection, feature clustering and cluster validation. This paper uses a Spiking Neural Network that is been trained with an Evolution topology algorithm and Genetic Algorithm is used to select the features from the dataset. The result of the network will cluster that classifies the data into abrupt types. The clusters are then validated using DB index.