In this article, we propose a novel Bayesian nonparametric clustering algorithm based on a Dirichlet process mixture of Dirichlet distributions which have been shown to be very flexible for modeling proportional data. The idea is to let the number of mixture components increases as new data to...

Binary data latent class analysis is a form of model-based clustering applied in a wide range of fields. A central assumption of this model is that of conditional independence of responses given latent class membership, often referred to as the “local independence” assumption. The results of...

Given a row-stochastic matrix describing pairwise similarities between data objects, spectral clustering makes use of the eigenvectors of this matrix to perform dimensionality reduction for clustering in fewer dimensions. One example from this class of algorithms is the Robust Perron Cluster...

In this paper, we develop a family of data clustering algorithms that combine the strengths of existing spectral approaches to clustering with various desirable properties of fuzzy methods. In particular, we show that the developed method “Fuzzy-RW,” outperforms other frequently used...

This paper investigate a class of semi-supervised support vector machines (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$\text{ S }^3\mathrm{VMs}$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mrow> <mspace width="4.pt"/> <mtext>S</mtext> <msup> <mspace width="4.pt"/> <mn>3</mn> </msup> <mi mathvariant="normal">VMs</mi> </mrow> </math> </EquationSource> </InlineEquation>) with arbitrary norm. A general framework for the <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$$\text{ S }^3\mathrm{VMs}$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mrow> <mspace width="4.pt"/> <mtext>S</mtext> <msup> <mspace width="4.pt"/> <mn>3</mn> </msup> <mi mathvariant="normal">VMs</mi> </mrow> </math> </EquationSource> </InlineEquation> was first constructed based on a robust DC (Difference of Convex...</equationsource></equationsource></inlineequation></equationsource></equationsource></inlineequation>

In model-based clustering and classification, the cluster-weighted model is a convenient approach when the random vector of interest is constituted by a response variable <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$Y$$</EquationSource> </InlineEquation> and by a vector <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$${\varvec{X}}$$</EquationSource> </InlineEquation> of <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$$p$$</EquationSource> </InlineEquation> covariates. However, its applicability may be limited when <InlineEquation ID="IEq4"> <EquationSource Format="TEX">$$p$$</EquationSource> </InlineEquation> is...</equationsource></inlineequation></equationsource></inlineequation></equationsource></inlineequation></equationsource></inlineequation>

We introduce a dimension reduction method for model-based clustering obtained from a finite mixture of <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$$t$$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mrow> <mi>t</mi> </mrow> </math> </EquationSource> </InlineEquation>-distributions. This approach is based on existing work on reducing dimensionality in the case of finite Gaussian mixtures. The method relies on identifying a reduced subspace of...</equationsource></equationsource></inlineequation>

This paper presents a finite mixture of multivariate betas as a new model-based clustering method tailored to applications where the feature space is constrained to the unit hypercube. The mixture component densities are taken to be conditionally independent, univariate unimodal beta densities...

Complex data such as those where each statistical unit under study is described not by a single observation (or vector variable), but by a unit-specific sample of several or even many observations, are becoming more and more popular. Reducing these sample data by summary statistics, like the...

Severe constraints imposed by the nature of endless sequences of data collected from unstable phenomena have pushed the understanding and the development of automated analysis strategies, such as data clustering techniques. However, current clustering validation approaches are inadequate to data...