Data Science, considered as a science by itself, is in general terms, the extraction of
knowledge from data. Symbolic data analysis (SDA) gives a new way of thinking in Data …

Symbolic Data Analysis (SDA) provides a framework for the representation and analysis of
data that comprehends inherent variability. While in Data Mining and classical Statistics the …

We consider the problem of parameter estimation in statistical models in the case where
data are uncertain and represented as belief functions. The proposed method is based on …

Interval-valued time series are interval-valued data that are collected in a chronological
sequence over time. This paper introduces three approaches to forecasting interval-valued …

This paper introduces symbolic data analysis, explaining how it extends the classical data
models to take into account more complete and complex information. Several examples …

Intelligently managing energy production and consumption on community basis can
enhance the demand side energy efficiency. The fact that the number of energy resources …

Interval-valued information systems are generalized models of single-valued information
systems. Accuracy and roughness are employed to depict the uncertainty of a set under an …

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 …

Covers everything readers need to know about clustering methodology for symbolic data—
including new methods and headings—while providing a focus on multi-valued list data …

Numerous studies have focused on clustering methods for interval-valued data, which is a
type of symbolic data. However, limited attention has been awarded to a clustering method …