This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries
to reflect the profile of this area by focusing more on those subjects that have been given …

In this paper, we present an algorithm for cluster analysis that integrates aspects from cluster
ensemble and multi-objective clustering. The algorithm is based on a Pareto-based multi …

Recent high throughput omics technology has been used to assemble large biomedical
omics datasets. Clustering of single omics data has proven invaluable in biomedical …

В монографии рассматривается эволюция представлений о типологическом методе в
социологии, место типологического анализа как метаметодики в структуре …

There is a large body of works on multi-view clustering that exploit multiple representations
(or views) of the same input data for better convergence. These multiple views can come …

This paper investigates a genetic programming (GP) approach aimed at the multi-objective
design of hierarchical consensus functions for clustering ensembles. By this means, data …

Clustering is a difficult task: there is no single cluster definition and the data can have more
than one underlying structure. Pareto-based multi-objective genetic algorithms (eg, MOCK …

Data clustering is an important technique to extract and understand relevant information in
large data sets. In this paper, a clustering algorithm based on graph theoretic models and …

Clustering is a data mining technique widely used to find similar group of data. A better
cluster always have most similar data while the elements from the different clusters are …

A Clusterização de dados em grupos oferece uma maneira de entender e extrair
informações relevantes de grandes conjuntos de dados. A abordagem em relação a …