A Weighted Genetic Algorithm Based Method for Clustering of Heteroscaled Datasets

This paper introduces a weighted genetic algorithm (GA) based clustering method for datasets with differently scaled dimensions. Several types of synthetic two dimensional scatter data were clustered using the typical k-means clustering method. The weighted GA-based clustering method was developed to address the problem of clustering data with differently scaled (heteroscaled) dimensions. Cluster analysis results obtained from using this method was compared to the results produced from the application of the traditional k-means clustering. By introducing weights in the fitness evaluation component of the meta-heuristic search method, a more efficient clustering of heteroscaled data was produced. In real applications, this method can be used in cluster analyses of scatter data with significantly different scales in dimensions, such as kurtosis versus fatigue damage relationship scatter data.