Abstract Multi-Source Information Fusion (MSIF) is a comprehensive and interdisciplinary
subject, and is referred to as, multi-sensor information fusion which was originated in the …

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

Time series data are commonly clustered based on their distributional characteristics. The
moments play a central role among such characteristics because of their relevant …

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 …

This paper contributes to the existing literature on the analysis of spatial time series
presenting a new clustering algorithm called COFUST, ie COpula-based FUzzy clustering …

We present a new way to find clusters in large vectors of time series by using a measure of
similarity between two time series, the generalized cross correlation. This measure …

The detection of patterns in multivariate time series is a relevant task, especially for large
datasets. In this paper, four clustering models for multivariate time series are proposed, with …

Clustering geographical units based on a set of quantitative features observed at several
time occasions requires to deal with the complexity of both space and time information. In …

The fuzzy approach to clustering arises to cope with situations where objects have not a
clear assignment. Unlike the hard/standard approach where each object can only belong to …