Data clustering using a model granular magnet

We present a new approach to clustering, based on the physical properties of an inhomogeneous ferromagnet. No assumption is made regarding the underlying distribution of the data. We assign a Potts spin to each data point and introduce an interaction between neighboring points, whose strength is a decreasing function of the distance between the neighbors. This magnetic system exhibits three phases. At very low temperatures it is completely ordered; all spins are aligned. At very high temperatures the system does not exhibit any ordering and in an intermediate regime clusters of relatively strongly coupled spins become ordered, whereas different clusters remain uncorrelated. This intermediate phase is identified by a jump in the order parameters. The spin-spin correlation function is used to partition the spins and the corresponding data points into clusters. We demonstrate on three synthetic and three real data sets how the method works. Detailed comparison to the performance of other techniques clearly indicates the relative success of our method.Comment: 46 pages, postscript, 15 ps figures include

ELABORAÇÃO DE UMA MATRIZ DE AVALIAÇÃO E MONITORAMENTO DE UMA FARMÁCIA HOSPITALAR

RESUMO Introdução: O acompanhamento e a avaliação continuada das ações desenvolvidas na Assistência Farmacêutica são estratégias para a busca da qualidade, especialmente no âmbito hospitalar. Uma boa avaliação visa reduzir incertezas, melhorar a efetividade das ações e propiciar a tomada de decisões relevantes auxiliando na gestão dos serviços. Objetivos: Elaborar uma matriz de avaliação e monitoramento da farmácia de uma instituição hospitalar. Método: Trata-se de um estudo avaliativo. Para a elaboração e validação de conteúdo foram desenvolvidas as seguintes etapas: revisão bibliográfica, proposição das dimensões e elaboração de indicadores, grupo focal com especialistas para validação e priorização das dimensões e aplicação da técnica delphi aos indicadores para relevância e valoração. O coeficiente alfa de Cronbach foi utilizado para medir a confiabilidade dos indicadores. O estudo foi desenvolvido no contexto da farmácia de um hospital de grande porte no Estado do Rio Grande do Sul. Resultados: Foram identificadas cinco dimensões para a avaliação da Farmácia Hospitalar e 70 indicadores, distribuídos da seguinte maneira: Gestão da Assistência (n=15), Gestão dos Recursos Humanos (n=13), Gestão do Medicamento (n=13), Gestão Financeira (n=12) e Gestão da Clínica dos Pacientes (n=17). A dimensão Gestão do Medicamento foi priorizada pelos especialistas, finalizada com nove indicadores. Conclusão: O estudo evidenciou a importância da avaliação para a gestão da Farmácia Hospitalar e a capacidade de execução de métodos de consenso para indicadores de qualidade em serviços de alto nível de complexidade. O instrumento com os indicadores desenvolvidos para dimensão Gestão do Medicamento mostrou evidência de validade e uma boa confiabilidade

Collaborative Privacy-Preserving Analysis of Oncological Data using Multiparty Homomorphic Encryption

Real-world healthcare data sharing is instrumental in constructing broader-based and larger clinical data sets that may improve clinical decision-making research and outcomes. Stakeholders are frequently reluctant to share their data without guaranteed patient privacy, proper protection of their data sets, and control over the usage of their data. Fully homomorphic encryption (FHE) is a cryptographic capability that can address these issues by enabling computation on encrypted data without intermediate decryptions, so the analytics results are obtained without revealing the raw data. This work presents a toolset for collaborative privacy-preserving analysis of oncological data using multiparty FHE. Our toolset supports survival analysis, logistic regression training, and several common descriptive statistics. We demonstrate using oncological data sets that the toolset achieves high accuracy and practical performance, which scales well to larger data sets. As part of this work, we propose a novel cryptographic protocol for interactive bootstrapping in multiparty FHE, which is of independent interest. The toolset we develop is general-purpose and can be applied to other collaborative medical and healthcare application domains

On Measuring Similarity Between Different Two-Layered Networks

In this paper we present a method for calculating ffl g , the generalization error of two-layered networks. ffl g is the fraction of the input space for which two networks yield different answers therefore it is a good index to measure the similarity between them. The method presented here is an extension of work reported previously. It is applied here to the case of a single-layer perceptron (which can be regarded as a particular two-layered perceptron) that tries to imitate a two-layered network. The particular realizations of such two-layered network that are analyzed here are the parity-machine, the and-machine and the committeemachine. We have also compared the input--output mapping of a committee and a parity machine. 02.70.-c, 87.22.Jb Typeset using REVT E X To appear in Journal of Physics A: math. and gen. I. INTRODUCTION Feedforward neural nets can be viewed as input-output devices whose parameters are tuned to perform a given function. An index of similarity between two s..

On the equivalence of Two Layered Perceptrons with Binary Neurons

We consider two-layered perceptrons consisting of N binary input units, K binary hidden units and one binary output unit, in the limit N AE K 1. We prove that the weights of a regular irreducible network are uniquely determined by its input-output map up to some obvious global symmetries. A network is regular if its K weight vectors from the input layer to the K hidden units are linearly independent. A (single layered) perceptron is said to be irreducible if its output depends on everyone of its input units; 2 and a two-layered perceptron is irreducible if the K + 1 perceptrons that constitute such network are irreducible. By global symmetries we mean, for instance, permuting the labels of the hidden units. Hence, two irreducible regular two-layered perceptrons that implement the same Boolean function must have the same number of hidden units, and must be composed of equivalent perceptrons. 1 Introduction In most applications dealing with learning and pattern classification, neur..

Clustering data through an analogy to the Potts model

A new approach for clustering is proposed. This method is based on an analogy to a physical model; the ferromagnetic Potts model at thermal equilibrium is used as an analog computer for this hard optimization problem. We do not assume any structure of the underlying distribution of the data. Phase space of the Potts model is divided into three regions; ferromagnetic, super-paramagnetic and paramagnetic phases. The region of interest is that corresponding to the super-paramagnetic one, where domains of aligned spins appear. The range of temperatures where these structures are stable is indicated by a non-vanishing magnetic susceptibility. We use a very efficient Monte Carlo algorithm to measure the susceptibility and the spin spin correlation function. The values of the spin spin correlation function, at the super-paramagnetic phase, serve to identify the partition of the data points into clusters. Many natural phenomena can be viewed as optimization processes, and the drive to understa..

Superparamagnetic Clustering Of Data: Application To Computer Vision

The aim of clustering is to partition data according to natural classes present in it. We proposed recently a method that makes no explicit assumption about the structure of the data and under very general and natural assumptions solves the clustering problem by evaluating thermal properties of a disordered (granular) magnet. The method was tested successfully on a variety of artificial and real-life problems; here we emphasize its application to analyze results obtained by a novel method of computer vision. The combination of these two techniques provides a powerful tool that succeeded to cluster properly 90 images of 6 objects on the basis of their pairwise dissimilarities. These dissimilarities, which constitute a highly nonmetric set of pairwise distances between the images, form the input for clustering. A hierarchical organization of the images that agrees with human intuition, was obtained without assigning to the images coordinates in some abstract space. 1 Introduction Imagi..