Operations research (OR) has been at the core of decision making since World War II, and
today, business interactions on different platforms have changed business dynamics …

Fuzzy clustering is an alternative method to conventional or hard clustering algorithms,
which makes partitions of data containing similar subjects. The tendency of adopting …

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 …

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 …

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 …

Building energy consumption accounts for a considerable proportion on energy
consumption. To reduce building energy consumption, building energy efficiency retrofitting …

In this paper, two fuzzy clustering methods for spatial interval-valued data are proposed, ie
the fuzzy C-Medoids clustering of spatial interval-valued data with and without entropy …

Along recent years, interval-valued rating scales have been considered as an alternative to
traditional single-point psychometric tools for human evaluations, such as Likert-type or …

Two versions of the generalized sign test and the signed‐rank test for interval‐valued data
(both for one‐sample and paired two‐sample problem) are proposed. These two versions …

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 …