Statistical analysis with missing data
Statistical analysis with missing data
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Identifiable finite mixtures of location models for clustering mixed-mode data
Statistics and Computing
MML clustering of multi-state, Poisson, vonMises circular and Gaussian distributions
Statistics and Computing
A k-mean clustering algorithm for mixed numeric and categorical data
Data & Knowledge Engineering
Cluster Analysis
Determining the number of clusters using information entropy for mixed data
Pattern Recognition
| Hi-index | 0.00 |
Mixture model clustering proceeds by fitting a finite mixture of multivariate distributions to data, the fitted mixture density then being used to allocate the data to one of the components. Common model formulations assume that either all the attributes are continuous or all the attributes are categorical. In this paper, we consider options for model formulation in the more practical case of mixed data: multivariate data sets that contain both continuous and categorical attributes. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 352–361 DOI: 10.1002/widm.33