Factor PD-Clustering

Factorial clustering methods have been developed in recent years thanks to the improving of computational power. These methods perform a linear transformation of data and a clustering on transformed data optimizing a common criterion. Factorial PD-clustering is based on Probabilistic Distance clustering (PD-clustering). PD-clustering is an iterative, distribution free, probabilistic, clustering method. Factor PD-clustering make a linear transformation of original variables into a reduced number of orthogonal ones using a common criterion with PD-Clustering. It is demonstrated that Tucker 3 decomposition allows to obtain this transformation. Factor PD-clustering makes alternatively a Tucker 3 decomposition and a PD-clustering on transformed data until convergence. This method could significantly improve the algorithm performance and allows to work with large dataset, to improve the stability and the robustness of the method

An extendable modular endoprosthetic system for bone tumour management in the leg

A modular endoprosthetic system has been developed at the Groningen University Hospital and the University of Twente. The system can bridge a defect resulting from the resection of a malignant bone tumour which has developed around the knee joint of a child. Since the other healthy leg continues to grow, the system includes an element whose length can be adjusted non-invasively by using an external magnetic field. In addition to this lengthening element, there are one hip and two knee components, connectors of various lengths, and fixation elements. The paper describes the elements of the modular endoprosthetic system. Tables are created by means of which the elemental composition of such an endoprosthesis can be determined for each individual patient

Comment on Kannemann's "The exact evaluation of 2-way cross-classifications"

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Comments on "A generalization of Fisher's exact test in PxQ contingency tables using more concordant relations"

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Survey of algorithms for exact distributions of test statistics in RxC contingency tables with fixed margins

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Modelling two parallel streams of observations with multinomial response models

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