Probabilistic K-mean with local alignment for clustering and motif discovery in functional data
share
We develop a new method to locally cluster curves and discover functional motifs, i.e. typical "shapes" that may recur several times along and across the curves capturing important local characteristics. In order to identify these shared curve portions, our method leverages ideas from functional data analysis (joint clustering and alignment of curves), bioinformatics (local alignment through the extension of high similarity seeds) and fuzzy clustering (curves belonging to more than one cluster, if they contain more than one typical "shape"). It can employ various dissimilarity measures and incorporate derivatives in the discovery process, thus exploiting complex facets of shapes. We demonstrate the performance of our method with an extensive simulation study, and show how it generalizes other clustering methods for functional data. Finally, we apply it to the discovery of functional motifs in "Omics" signals related to mutagenesis and genome dynamics.
READ FULL TEXT