Recombining partitions from multivariate data: a clustering method on Bayes factors

By Adolfo Álvarez, Daniel Peña in Research

March 1, 2014



We introduce SAGRA (Split And Group Recombining Algorithm), a cluster analysis methodology which split the data set into small homogeneous groups and later recombine those groups using Bayes factors. We compare the performance of SAGRA with other three cluster analysis algorithms: SAR, M-clust and K-means, using five quality measures: Purity, number of groups, Rand index, adjusted Rand index, and F1, over four different data configurations. Results indicate that the SAGRA algorithm obtain consistently similar or better indexes than the other algorithms over all measures and data configurations

Posted on:
March 1, 2014
1 minute read, 88 words
Cluster analysis Bayes factors SAR SAGRA.
See Also:
Recombining observations in cluster analysis: the SAGRA method
Recombining partitions via unimodality tests
Recombining dependent data: an Order Statistics approach