Unsupervised hierarchical clustering via a genetic algorithm

Abstract

We present a clustering algorithm which is unsupervised, incremental, and hierarchical. The algorithm is distance-based and creates centroids. Then we combine the power of evolutionary forces with the clustering algorithm, counting on good clusterings to evolve to yet better ones. We apply our approach to standard data sets, and get very good results. Finally, we use bagging to pool the results of different clustering trials, and again get very good results.