Evaluation of differential evolution and K-means algorithms on medical diagnosis

Abstract

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.

Evaluation of Differential Evolution Algorithm with Various Mutation Strategies for Clustering Problems

“…Minimum SSE indicates better cluster solutions. • Quantization error [16]: It calculates the average distance between data points and the cluster center as follows: (11) Where k indicate the number of clusters, C j stands for the jth cluster, d is a data point in C j , c j is the center of C j , |𝐶 𝑗 | is the number of data points in Cj and Dist is the Euclidean distance between data point d and the center c j of the cluster C j . Lower quantization means the better cluster results.…”