Clustering, or unsupervised classification, is a task often plagued by outliers. Yet there is a paucity of work on handling outliers in clustering. Outlier identification algorithms tend to fall into three broad categories: outlier inclusion …

Probability density machine (PDM) is a novel algorithm which was proposed recently for addressing class imbalance learning (CIL) problem. PDM can capture priori data distribution information well and present robust performance in various CIL …

This paper introduces a novel supervised classification method based on dynamic clustering (DC) and K-nearest neighbor (KNN) learning algorithms, denoted DC-KNN. The aim is to improve the accuracy of a classifier by using a DC method to discover …

Data clustering is an important task in the field of data mining. In many real applications, clustering algorithms must consider the order of data, resulting in the problem of clustering sequential data. For instance, analyzing the moving pattern …

The family of multiple scaled mixtures of multivariate normal (MSMN) distributions has been shown to be a powerful tool for modeling data that allow different marginal amounts of tail weight. An extension of the MSMN distribution is proposed …

We propose a discrete random effects multinomial regression model to deal with estimation and inference issues in the case of categorical and hierarchical data. Random effects are assumed to follow a discrete distribution with an a priori unknown …

Binary metaheuristic algorithms prove to be invaluable for solving binary optimization problems. This paper proposes a binary variant of the peacock algorithm (PA) for feature selection. PA, a recent metaheuristic algorithm, is built upon lekking …

We propose a variable selection method for multivariate hidden Markov models with continuous responses that are partially or completely missing at a given time occasion. Through this procedure, we achieve a dimensionality reduction by selecting …

Normal cluster-weighted models constitute a modern approach to linear regression which simultaneously perform model-based cluster analysis and multivariate linear regression analysis with random quantitative regressors. Robustified models have …

Nowadays, an increasing number of problems involve data with one infinite continuous dimension known as functional data. In this paper, we introduce the funLOCI algorithm, which enables the identification of functional local clusters or functional …

Model-based clustering tackles the task of uncovering heterogeneity in a data set to extract valuable insights. Given the common presence of outliers in practice, robust methods for model-based clustering have been proposed. However, the use of …

A dissimilarity d on a set S of size n is said to be Robinson if its matrix can be symmetrically permuted so that its elements do not decrease when moving away from the main diagonal along any row or column. Equivalently, S admits a total order < …

Two families of matrix-variate hidden Markov regression models (MV-HMRMs) are here introduced. The distinction between them relies on the role of the covariates, which can be treated as fixed or random. Parsimony is achieved by using the …