FPDC: PD-clustering and factor PD-clustering
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 centre is a constant. PD-clustering is a flexible method that can be used with non-spherical clusters, outliers, or noisy data. Facto PD-clustering (FPDC) is a recently proposed factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It allows clustering of high dimensional data sets.
||Cristina Tortora and Paul D. McNicholas
||Cristina Tortora <ctortora at uoguelph.ca>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]