Bayesian principal curve clustering by species-sampling mixture models Clustering mediante modelli mistura a campionamento di specie di curve principali bayesiane

Raffaele Argiento, Alessandra Guglielmi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Abstract In this work we are interested in clustering data whose support is “curved”. For this purpose, we will follow a Bayesian nonparametric approach by considering a species sampling mixture model. Our first goal is to define a general/flexible class of distributions, such that they can model data from clusters with non standard shape. To this end, we extend the definition of principal curve given in [8] (Tibshirani 1992) into a Bayesian framework. We propose a new hierarchical model, where the data in each cluster are parametrically distributed around the Bayesian principal curve, and the prior cluster assignment is given on the latent variables at the second level of hierarchy according to a species sampling model. As an application we will consider the detection of seismic faults using data coming from Italian earthquake catalogues.
Original languageEnglish
Title of host publicationProceedings of 47th SIS Scientific Meeting of the Italian Statistica Society
Pages1-6
Number of pages6
Publication statusPublished - 2014
Event47th SIS Scientific Meeting of the Italian Statistica Society - Cagliari
Duration: 11 Jun 201413 Jun 2014

Conference

Conference47th SIS Scientific Meeting of the Italian Statistica Society
CityCagliari
Period11/6/1413/6/14

Keywords

  • Cluster Analysis, Mixture Models, Principal Curve, Specie Sampling Models

Fingerprint Dive into the research topics of 'Bayesian principal curve clustering by species-sampling mixture models Clustering mediante modelli mistura a campionamento di specie di curve principali bayesiane'. Together they form a unique fingerprint.

Cite this