The beginning of the age of artificial intelligence and machine learning has created new
challenges and opportunities for data analysts, statisticians, mathematicians …

In finance, cluster analysis is a tool particularly useful for classifying stock market
multivariate time series data related to daily returns, volatility daily stocks returns, commodity …

In this paper we propose different robust fuzzy clustering models for classifying
heteroskedastic (volatility) time series, following the so-called model-based approach to time …

A fuzzy clustering model for data with mixed features is proposed. The clustering model
allows different types of variables, or attributes, to be taken into account. This result is …

In spatiotemporal data commonly encountered in geographical systems, biomedical signals,
and the like, each datum is composed of features comprising a spatial component and a …

Traditional and fuzzy cluster analyses are applicable to variables whose values are
uncorrelated. Hence, in order to cluster time series data which are usually serially …

We investigate the fuzzy clustering of interval time series using wavelet variances and
covariances; in particular, we use a fuzzy c-medoids clustering algorithm. Traditional …

A novel procedure to perform fuzzy clustering of multivariate time series generated from
different dependence models is proposed. Different amounts of dissimilarity between the …

In this paper, following the Partitioning Around Medoids (PAM) approach and the fuzzy
theory, we propose a clustering model for financial time series based on the estimated …

In recent years, the research of statistical methods to analyze complex structures of data has
increased. In particular, a lot of attention has been focused on the interval-valued data. In a …