Gene expression data hide vital information required to understand the biological process
that takes place in a particular organism in relation to its environment. Deciphering the …

Cluster ensembles have been shown to be better than any standard clustering algorithm at
improving accuracy and robustness across different data collections. This meta-learning …

Cluster ensembles combine multiple clusterings of a set of objects into a single consolidated
clustering, often referred to as the consensus solution. Consensus clustering can be used to …

Many clustering algorithms, including cluster ensembles, rely on a random component.
Stability of the results across different runs is considered to be an asset of the algorithm. The …

Cluster ensembles have recently emerged as a powerful alternative to standard cluster
analysis, aggregating several input data clusterings to generate a single output clustering …

Data is useless if information or knowledge that can be used for further reasoning cannot be
inferred from it. Cluster analysis, based on some criteria, shares data into important, practical …

Técnicas de Agrupamento Page 1 Revista de Sistemas de Informação da FSMA n. 4 (2009) pp.
18-36 http://www.fsma.edu.br/si/sistemas.html 18 Técnicas de Agrupamento Ricardo Linden …

Adjusted Rand index is used to measure diversity in cluster ensembles and a diversity
measure is subsequently proposed. Although the measure was found to be related to the …

Although attempts have been made to solve the problem of clustering categorical data via
cluster ensembles, with the results being competitive to conventional algorithms, it is …

The idea of ensemble methodology is to build a predictive model by integrating multiple
models. It is well-known that ensemble methods can be used for improving prediction …