Artificial neural network has been widely used in various fields as an intelligent tool in recent years, such as artificial intelligence, pattern recognition, medical diagnosis, machine learning and so on. The classification of breast cancer is a medical application that poses a great challenge for researchers and scientists. Recently, the neural network has become a popular tool in the classification of cancer datasets. Classification is one of the most active research and application areas of neural networks. Major disadvantages of artificial neural network (ANN) classifier are due to its sluggish convergence and always being trapped at the local minima. To overcome this problem, differential evolution algorithm (DE) has been used to determine optimal value or near optimal value for ANN parameters. DE has been applied successfully to improve ANN learning from previous studies. However, there are still some issues on DE approach such as longer training time and lower classification accuracy. To overcome these problems, island based model has been proposed in this system. The aim of our study is to propose an approach for breast cancer distinguishing between different classes of breast cancer. This approach is based on the Wisconsin Diagnostic and Prognostic Breast Cancer and the classification of different types of breast cancer datasets. The proposed system implements the island-based training method to be better accuracy and less training time by using and analysing between two different migration topologies.
Clustering (or cluster analysis) aims to organize a collection of data items into clusters, such that items within a cluster are more "similar" to each other than they are to items in the other clusters. There are many applications for clustering such as image segmentation, marketing, ecommerce, business, scientific and engineering. The K-means has served as the most widely used partitioned clustering algorithm. However, in most cases it provides only locally optimal solutions. Evolutionary algorithm such as genetic algorithm and differential evolution can be used to find global optimal solution for optimization problem. Clustering can be regarded as optimization problem of finding optimal partition of data according to cluster validity measures. Differential evolution (DE) algorithm is a novel evolutionary algorithm (EA) for global optimization, where the mutation operator is based on the distribution of solutions in the population. The paper presents the differential evolution for clustering and compares the purity result with K-means algorithm. The empirical studying is conducted on three medical datasets; Pima, Liver, Heart from UCI data repository.
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