Authors: Bayá, A. E.; Granitto, P. M.
Resumen: In this work we use the recently introduced PKNNG metric, associated with a simple Hierarchical Clustering (HC) method, to find accurate an stable solution for the clustering of gene expression datasets. On real world problems it is important to evaluate the quality of the clustering process. According to this, we use a suitable framework to analyze the stability of the clustering solution obtained by HC+PKNNG. Using an artificial problem and two gene expression datasets, we show that the PKNNG metric gives better solutions than the Euclidean method, and that those solutions are stable. Our results show the potential of the association of the PKNNG metric based clustering with the stability analysis for the class discovery process in high--throughput data.
Meeting type: Congreso.
Type of job: Artículo Completo.
Production: Clustering gene expression data with the PKNNG metric.
Scientific meeting: XIV Congreso Argentino de Ciencias de la Computación - CACIC 2008.
Meeting place: Chilecito, Argentina.
Organizing Institution: Red UNCI.
It's published?: Yes
Publication place: Argentina
Meeting month: 12