Clustering is a powerful vehicle to reveal and visualize structure of data. When dealing with
time series, selecting a suitable measure to evaluate the similarities/dissimilarities within the …

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

Existing regionalization methods have largely overlooked the temporal dimension, leading
to outcomes that predominantly reflect spatial differentiation of regional variables only at a …

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 …

Pattern recognition is a collection of computer techniques to classify various observations
into different clusters of similar attributes in either supervised or unsupervised manner …

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 …

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 …

In this paper, time-series clustering is discussed. At first ℓ 1 trend filtering method is used to
produce an optimal segmentation of time series. Next optimized fuzzy information …

The literature on time-series clustering methods has increased considerably over the last
two decades with a wide range of applications in many different fields, including geology …

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