Wavelet-based Clustering and Classification
The most important techniques in Statistical Learning are prediction, classification and clustering. The first two belong to Supervised Learning and, the third, to Unsupervised Learning. In this talk I consider several wavelet-based procedures for clustering and classification purposes. In some situations, the time domain approach may not lead to clear classification or discrimination. When we move to the wavelet domain, the multiresolution analysis leads to looking at data in several levels of resolution (or scales) and then the separation may become better. The talk will not be technical and the procedures will be Illustrated through practical examples.