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Stability-Based Model Selection for High Throughput Genomic Data: An Algorithmic Paradigm

  • Raffaele Giancarlo
  • Filippo Utro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7597)

Abstract

Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. In this beautiful area, one of the most difficult challenges is the model selection problem, i.e., the identification of the correct number of clusters in a dataset. In the last decade, a few novel techniques for model selection, representing a sharp departure from previous ones in statistics, have been proposed and gained prominence for microarray data analysis. Among those, the stability-based methods are the most robust and best performing in terms of prediction, but the slowest in terms of time. Unfortunately, this fascinating and classic area of statistics as model selection, with important practical applications, has received very little attention in terms of algorithmic design and engineering. In this paper, in order to partially fill this gap, we highlight: (A) the first general algorithmic paradigm for stability-based methods for model selection; (B) a novel algorithmic paradigm for the class of stability-based methods for cluster validity, i.e., methods assessing how statistically significant is a given clustering solution; (C) a general algorithmic paradigm that describes heuristic and very effective speed-ups known in the Literature for stability-based model selection methods.

Keywords

Cluster Algorithm Cluster Solution Microarray Data Analysis Consensus Cluster Model Selection Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Raffaele Giancarlo
    • 1
  • Filippo Utro
    • 2
  1. 1.Dipartimento di Matematica ed InformaticaUniversity of PalermoPalermoItaly
  2. 2.Computational Biology CenterIBM T.J. Watson Research CenterYorktown HeightsUSA

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