Detection of elliptical shapes via cross-entropy clustering

The problem of finding elliptical shapes in an image will be considered. We discuss the solution which uses cross-entropy clustering. The proposed method allows the search for ellipses with predefined sizes and position in the space. Moreover, it works well for search of ellipsoids in higher dimensions

Mixture of latent trait analyzers for model-based clustering of categorical data

Model-based clustering methods for continuous data are well established and commonly used in a wide range of applications. However, model-based clustering methods for categorical data are less standard. Latent class analysis is a commonly used method for model-based clustering of binary data and/or categorical data, but due to an assumed local independence structure there may not be a correspondence between the estimated latent classes and groups in the population of interest. The mixture of latent trait analyzers model extends latent class analysis by assuming a model for the categorical response variables that depends on both a categorical latent class and a continuous latent trait variable; the discrete latent class accommodates group structure and the continuous latent trait accommodates dependence within these groups. Fitting the mixture of latent trait analyzers model is potentially difficult because the likelihood function involves an integral that cannot be evaluated analytically. We develop a variational approach for fitting the mixture of latent trait models and this provides an efficient model fitting strategy. The mixture of latent trait analyzers model is demonstrated on the analysis of data from the National Long Term Care Survey (NLTCS) and voting in the U.S. Congress. The model is shown to yield intuitive clustering results and it gives a much better fit than either latent class analysis or latent trait analysis alone

Standardising the lift of an association rule

The lift of an association rule is frequently used, both in itself and as a component in formulae, to gauge the interestingness of a rule. The range of values that lift may take is used to standardise lift so that it is more effective as a measure of interestingness. This standardisation is extended to account for minimum support and confidence thresholds. A method of visualising standardised lift, through the relationship between lift and its upper and lower bounds, is proposed. The application of standardised lift as a measure of interestingness is demonstrated on college application data and social questionnaire data. In the latter case, negations are introduced into the mining paradigm and an argument for this inclusion is put forward. This argument includes a quantification of the number of extra rules that arise when negations are considered.

Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models

Model-based clustering using a family of Gaussian mixture models, with parsimonious factor analysis like covariance structure, is described and an efficient algorithm for its implementation is presented. This algorithm uses the alternating expectation-conditional maximization (AECM) variant of the expectation-maximization (EM) algorithm. Two central issues around the implementation of this family of models, namely model selection and convergence criteria, are discussed. These central issues also have implications for other model-based clustering techniques and for the implementation of techniques like the EM algorithm, in general. The Bayesian information criterion (BIC) is used for model selection and Aitken's acceleration, which is shown to outperform the lack of progress criterion, is used to determine convergence. A brief introduction to parallel computing is then given before the implementation of this algorithm in parallel is facilitated within the master-slave paradigm. A simulation study is then carried out to confirm the effectiveness of this parallelization. The resulting software is applied to two datasets to demonstrate its effectiveness when compared to existing software.

The BBC and Television Fame in the 1950's: Living with The Grove Family (1954-7) and going Face to Face (1959-62) with television

This article aims to contribute to historical knowledge about television's relations with fame, while simultaneously exploring the conceptual tools used to study this field. With this in mind, this article examines two case studies from the 1950s: the BBC's popular serial The Grove Family and the interview-in-depth programme Face to Face . A key aim is to draw out the different meanings which circulated around television's relations with fame. Television has always constructed its own `personalities' (the Groves), while simultaneously circulating personae `outside' of their primary public or media roles (Face to Face). The article suggests that returning to this earlier context raises important questions. Where do these later conceptual claims of television fame locate their historical roots? To what extent were the debates about television fame a continuation of those surrounding radio? And to what degree are concepts such as `ordinariness' historical