A tutorial on manifold clustering using genetic algorithms

Abstract

Automatic Manifold identification is currently a challenging problem in Machine Learning. This process consists on separating a dataset blindly, according to the form defined by the data instances in the space. Data are discriminated in groups defined by their form. These approaches are usually focused on continuity-based methods where the manifold follows a continuity criterion. Currently, clustering techniques try to deal with the discrimination process, but there are a few algorithms that can generate an accurate and robust discrimination. This tutorial aims to present new different approaches, specially focused on Genetic Algorithms, which can deal with these problems.