08: Clustering Multivariate Discrete Data with Partial Records

Music City Center 

Being able to cluster data with incomplete records is vital in many disciplines. Here, we develop a model-based clustering approach for clustering multivariate discrete data with missing entries using a mixture of multivariate Poisson lognormal distributions. A multivariate Poisson lognormal distribution is a hierarchical Poisson distribution that can account for over-dispersion and can model the correlation between the variables. To illustrate the effectiveness of this method, we have designed a variety of simulation studies to show the robustness of this new method under different percentages of incomplete records and patterns of missing data. Additionally, the approach is used to demonstrate clustering partial records from a proteomics dataset.

Clustering

Missing Data

Discrete Data

Multivariate Poisson Log Normal Distribution 

Biometrics Section