TY - JOUR
AU - Erisoglu, Murat
AB - Clustering analysis based on a mixture of multivariate normal distributions is commonly used in the clustering of multidimensional data sets. Model selection is one of the most important problems in mixture cluster analysis based on the mixture of multivariate normal distributions. Model selection involves the determination of the number of components (clusters) and the selection of an appropriate covariance structure in the mixture cluster analysis. In this study, the efficiency of information criteria that are commonly used in model selection is examined. The effectiveness of information criteria has been determined according to the success in the selection of the number of components and in the selection of an appropriate covariance matrix.
TI - A Comparison of Information Criteria in Clustering Based on Mixture of Multivariate Normal Distributions
JF - Mathematical and Computational Applications
DO - 10.3390/mca21030034
DA - 2016-08-01
UR - https://www.deepdyve.com/lp/multidisciplinary-digital-publishing-institute/a-comparison-of-information-criteria-in-clustering-based-on-mixture-of-D98PQ8ur8q
SP - 34
VL - 21
IS - 3
DP - DeepDyve
ER -