Résumé. Le clustering semi-supervisé combine l’apprentissage supervisé and non-supervisé pour produire meilleurs clusterings. Dans la phase initiale supervisée de l’algorithme, un échantillon d’apprentissage est produit par selection aléatoire. On suppose que les exemples de l’échantillon d’apprentissage sont étiquetés par un attribut de classe. Puis, un algorithme incrémentiel développé pour l...

This paper demonstrates the utility of multilevel Bayesian models of data annotation for classifiers. The observable data is the set of categorizations of items by annotators from which data may be missing at random or may be replicated. Estimated individuallevel parameters include category prevalence, the “true” category of each item, and the accuracy in terms of sensitivity and specificity of...

In this paper, we cast the classic problem of achieving k-anonymity for a given database as a problem in algebraic topology. Using techniques from this field of mathematics, we propose a framework for k-anonymity that brings new insights and algorithms to anonymize a database. We begin by addressing the simpler case when the data lies in a metric space. This case is instrumental to introduce th...

Nonresponse is common in surveys. When the response probability of a survey variable Y depends on Y through an observed auxiliary categorical variable Z (i.e., the response probability of Y is conditionally independent of Y given Z), a simple method often used in practice is to use Z categories as imputation cells and construct estimators by imputing nonrespondents or reweighting respondents wi...

In this paper we provide asymptotic theory of local maximum likelihood techniques for estimating a regression model where some regressors are discrete. Our methodology and theory are particularly useful for models that give us a likelihood of the unknown functions we can use to identify and estimate the underlying model. This is the case when the conditional density of the variable of interest,...

Locating and evaluating relationships among values in multiple streams of data is a di cult and important task. Consider the data owing from monitors in an intensive care unit. Readings from various subsets of the monitors are indicative and predictive of certain aspects of the patient's state. We present an algorithm that facilitates discovery and assessment of the strength of such predictive ...

A tree-structured classifier is a decision tree for predicting a class variable from one or more predictor variables. THAID [15, 7] was the first such algorithm. This article focuses on the CART R © [2], C4.5 [17], and GUIDE [12] methods. The algorithms are briefly reviewed and their similarities and differences compared on a real data set and by simulation. In a typical classification problem,...

A tree-structured classifier is a decision tree for predicting a class variable from one or more predictor variables. THAID [15, 7] was the first such algorithm. This article focuses on the CART R © [2], C4.5 [17], and GUIDE [12] methods. The algorithms are briefly reviewed and their similarities and differences compared on a real data set and by simulation. In a typical classification problem,...

This paper is a chronological survey, with no proofs, of a direction in categorical algebra, which is based on categorical Galois theory and involves generalized central extensions, commutators, and internal groupoids in Barr exact Mal’tsev and more general categories. Galois theory proposes a notion of central extension, and motivates the study of internal groupoids, which is then used as an a...

Tourism in Perú has become very important, since there is a growing number of tourists arriving each year. This paper focus in understand what do speakingenglish tourists have in consideration when they visit Perú. We obtained all the tweets published in english during the year 2016, filtered by touristic places visited. In total, more than 192 thousand tweets were collected. We performed diffe...