ManiSonS: A New Visualization Tool for Manifold Clustering

Manifold learning is an important theme in machine learning. This paper proposes a new visualization approach to manifold clustering. The method is based on pie charts in order to obtain meaningful visualizations of the clustering results when applying a manifold technique. In addition to this, the proposed approach extracts all the existing relationships among the attributes of the different clusters and find the most important variables of the manifold in order to distinguish among the different clusters. The methodology is tested in one synthetic data set and one real data set. Achieved results show the suitability and usefulness of the proposed approach.