EML for Unsupervised Learning

This chapter introduces the use of Evolutionary Machine Learning (EML) techniques for unsupervised machine learning tasks. First, a brief introduction to the main concepts related to unsupervised Machine Learning (ML) is presented. Then, an overview of the main EML approaches to these tasks is given together with a discussion of the main achievements and current challenges in addressing these tasks. We focus on four commonly found unsupervised learning tasks: Data preparation, Outlier detectionOutlier detection, Dimensionality reductionDimensionality reduction, and Association rule mining. Finally, we present a number of findings from the review. These findings could guide the reader at the time of applying EML techniques to unsupervised ML tasks or when developing new EML approaches.