This study uses fuzzy clustering technique to develop a predictive model for interval time
series. The proposed model is divided into three phases, with improvements built for each …

Millions of papers are submitted and published every year, but researchers often do not
have much information about the journals that interest them. In this paper, we introduced the …

Interval-valued functional clustering is a novel approach for functional data analysis where
each observation is represented by an interval. Existing interval-valued functional clustering …

The outlier elements of a data are ones that differs significantly from others. For many
reasons, we have to face with outlier elements in data analysis for the different fields …

Data analysis plays an indispensable role in understanding different phenomena. One of the
vital means of handling these data is to group them into a set of clusters given a measure of …

Kohonen maps, also known as self-organizing maps, is a powerful clustering method which
groups data using multiple nodes that converge to clusters. Therefore, it is not necessary to …

Due to the fact of uncertainty contained in observed real-valued time series, the aim of this
paper is to explore interval implicitization in real-valued time series classification problems …

Compression algorithms have been proposed with the technology advance. However, there
are not objective analysis procedures to guide a future choice of an algorithm directed for …

Os sensores baseados em Ressonância de Plasmons de Superfície (RPS) permitem avaliar
mudanças de índice de refração, com objetivo de detectar interações moleculares em …

Compression algorithms have been proposed with the technology advance. However, there
are not objective analysis procedures to guide a future choice of an algorithm directed for …