PD-Clustering and Related Methods
Factor probabilistic distance clustering
Gaussian PD-Clustering
Probabilistic Distance Clustering
Probabilistic Distance Clustering Adjusted for Cluster Size
Plots for FPDclustering objects
Print for FPDclustering objects
Probabilistic silhouette plot
Summary for FPDclusteringt Objects
Student-t PD-Clustering
Choice of the number of Tucker 3 factors for FPDC
Probabilistic distance clustering (PD-clustering) is an iterative, distribution-free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership under the constraint that the product of the probability and the distance of each point to any cluster center is a constant. PD-clustering is a flexible method that can be used with elliptical clusters, outliers, or noisy data. PDQ is an extension of the algorithm for clusters of different sizes. GPDC and TPDC use a dissimilarity measure based on densities. Factor PD-clustering (FPDC) is a factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It works on high-dimensional data sets.