Abstract
In this work we introduce a modification to an automatic non-supervised rule to select the parameters of a previously presented graph-based metric. This rule maximizes a clustering quality index providing the best possible solution from a clustering quality point of view. We apply our parameter-free PKNNG metric on gene expression data to show that the best quality solutions are also the ones that are more related to the biological classes. Finally, we compare our parameter-free metric with a group of state-of-the-art clustering algorithms. Our results indicate that our parameter-free metric performs as well as the state-of-the-art clustering methods.
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Bayá, A.E., Granitto, P.M. (2011). Improved Gene Expression Clustering with the Parameter-Free PKNNG Metric. In: Norberto de Souza, O., Telles, G.P., Palakal, M. (eds) Advances in Bioinformatics and Computational Biology. BSB 2011. Lecture Notes in Computer Science(), vol 6832. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22825-4_7
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DOI: https://doi.org/10.1007/978-3-642-22825-4_7
Publisher Name: Springer, Berlin, Heidelberg
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