Clustering algorithms aim at finding dense regions of data based on similarities and
dissimilarities of data points. Noise and outliers contribute to the computational procedure of …

Abstract Background Medical Image segmentation is an important image processing step.
Comparing images to evaluate the quality of segmentation is an essential part of measuring …

Big data has increasingly become predominant in many research fields affecting human
knowledge, including medicine and engineering. Cluster analysis, or clustering, is widely …

Very large (VL) data or big data are any data that you cannot load into your computer's
working memory. This is not an objective definition, but a definition that is easy to …

Fuzzy clustering algorithms generally treat data points with feature components under equal
importance. However, there are various datasets with irrelevant features involved in …

In unsupervised machine learning, agreement between partitions is commonly assessed
with so-called external validity indices. Researchers tend to use and report indices that …

In finance, cluster analysis is a tool particularly useful for classifying stock market
multivariate time series data related to daily returns, volatility daily stocks returns, commodity …

In the clustering ensemble the quality of base-clusterings influences the consensus
clustering. Although some researches have been devoted to weighting the base-clustering …

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 this paper, we introduce a fuzzy extension of a class of measures to compare clustering
structures, namely, measures that are based on the number of concordant and the number …