The em algorithm can be used to compute maximum likelihood estimates of model parameters for skew-t mixture models. We show that the intractable expectations needed in the e-step can be written out analytically. These closed form expressions bypass the need for numerical estimation procedures,...

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 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...

A dimension reduction method for model-based clustering via a finite mixture of shifted asymmetric Laplace distributions is introduced. The approach is based on existing work within the Gaussian paradigm and relies on identification of a reduced subspace. This subspace contains linear...

A novel model-based classification technique is introduced based on mixtures of multivariate t-distributions. A family of four mixture models is defined by constraining, or not, the covariance matrices and the degrees of freedom to be equal across mixture components. Parameters for each of the...

A mixture of skew-t factor analyzers is introduced as well as a family of mixture models based thereon. The particular formulation of the skew-t distribution used arises as a special case of the generalized hyperbolic distribution. Like their Gaussian and t-distribution analogues, mixtures of...

Parsimonious mixtures of multivariate t-factor analyzers are used for robust clustering of high-dimensional data. Sixteen parsimonious mixtures of t-factor analyzers are utilized and the AECM algorithm is used for parameter estimation. Application to compact facial representation is illustrated.

Robust mixture modeling approaches using skewed distributions have recently been explored to accommodate asymmetric data. Parsimonious skew-t and skew-normal analogues of the GPCM family that employ an eigenvalue decomposition of a scale matrix are introduced. The methods are compared to...

A mixture of latent trait models with common slope parameters for model-based clustering of high-dimensional binary data, a data type for which few established methods exist, is proposed. Recent work on clustering of binary data, based on a d-dimensional Gaussian latent variable, is extended by...