An adaptive meta-clustering approach: combining the information from different clustering results.
Proc IEEE Comput Soc Bioinform Conf
; 1: 276-87, 2002.
Article
em En
| MEDLINE
| ID: mdl-15838144
ABSTRACT
With the development of microarray techniques, there is an increasing need of information processing methods to analyze the high throughput data. Clustering is one of the most promising candidates because of its simplicity, flexibility and robustness. However, there is no "perfect" clustering approach outperforming its counterparts, and it is hard to evaluate and combine the results from different techniques, especially in a field without much prior knowledge, such as bioinformatics. This paper proposes a meta-clustering approach to extract the information from results of different clustering techniques, so that a better interpretation of the data distribution can be obtained. A special distance measure is defined to represent the statistical "signal" of each cluster produced by various clustering techniques. The algorithm is applied on both artificial and real data Simulations show that the proposed approach is able to extract the information efficiently and accurately from the input clustering structure.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Reconhecimento Automatizado de Padrão
/
Análise por Conglomerados
/
Análise de Sequência de DNA
/
Análise de Sequência com Séries de Oligonucleotídeos
/
Perfilação da Expressão Gênica
Tipo de estudo:
Evaluation_studies
Idioma:
En
Revista:
Proc IEEE Comput Soc Bioinform Conf
Ano de publicação:
2002
Tipo de documento:
Article