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 works on high dimensional datasets.
|Author||Cristina Tortora and Paul D. McNicholas|
|Date of publication||2017-08-23 18:08:58 UTC|
|Maintainer||Cristina Tortora <firstname.lastname@example.org>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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