Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probability Density Decomposition for Conditionally Dependent Random Variables Modeled by Vines
Annals of Mathematics and Artificial Intelligence
Robust mixture modelling using the t distribution
Statistics and Computing
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
An Introduction to Copulas (Springer Series in Statistics)
An Introduction to Copulas (Springer Series in Statistics)
A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Divergence estimation for multidimensional densities via k-nearest-neighbor distances
IEEE Transactions on Information Theory
On the simplified pair-copula construction - Simply useful or too simplistic?
Journal of Multivariate Analysis
Online heterogeneous mixture modeling with marginal and copula selection
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition
| Hi-index | 0.01 |
Finite mixtures are often used to perform model based clustering of multivariate data sets. In real life applications, such data may exhibit complex nonlinear form of dependence among the variables. Also, the individual variables (margins) may follow different families of distributions. Most of the existing mixture models are unable to accommodate these two aspects of the data. This paper presents a finite mixture model that involves a pair-copula based construction of a multivariate distribution. Such a model de-couples the margins and the dependence structures. Hence, the margins can be modeled using different families. Again, many possible dependence structures can also be studied using different copulas. The resulting mixture model (called DVMM) is then capable of capturing a broad family of distributions including non-Gaussian models. Here we study DVMM in the context of clustering of multivariate data. We design an expectation maximization procedure for estimating the mixture parameters. We perform extensive experiments on the basis of a number of well-known data sets. A detailed evaluation of the clustering quality obtained by DVMM in comparison to other mixture models is presented. The experimental results show that the performance of DVMM is quite satisfactory.