FPF-SB: A Scalable Algorithm for Microarray Gene Expression Data Clustering

  • Filippo Geraci
  • Mauro Leoncini
  • Manuela Montangero
  • Marco Pellegrini
  • M. Elena Renda
Conference paper

DOI: 10.1007/978-3-540-73321-8_69

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4561)
Cite this paper as:
Geraci F., Leoncini M., Montangero M., Pellegrini M., Renda M.E. (2007) FPF-SB: A Scalable Algorithm for Microarray Gene Expression Data Clustering. In: Duffy V.G. (eds) Digital Human Modeling. ICDHM 2007. Lecture Notes in Computer Science, vol 4561. Springer, Berlin, Heidelberg

Abstract

Efficient and effective analysis of large datasets from microarray gene expression data is one of the keys to time-critical personalized medicine. The issue we address here is the scalability of the data processing software for clustering gene expression data into groups with homogeneous expression profile. In this paper we propose FPF-SB, a novel clustering algorithm based on a combination of the Furthest-Point-First (FPF) heuristic for solving the k-center problem and a stability-based method for determining the number of clusters k. Our algorithm improves the state of the art: it is scalable to large datasets without sacrificing output quality.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Filippo Geraci
    • 1
    • 3
  • Mauro Leoncini
    • 1
    • 2
  • Manuela Montangero
    • 1
    • 2
  • Marco Pellegrini
    • 1
  • M. Elena Renda
    • 1
  1. 1.CNR, Istituto di Informatica e Telematica, via Moruzzi 1, 56124, Pisa, (Italy) 
  2. 2.Dipartimento di Ingegneria dell’Informazione, Università di Modena e Reggio Emilia, Via Vignolese 905 - 41100 Modena (Italy) 
  3. 3.Dipartimento di Ingegneria dell’Informazione, Università di Siena, Via Roma 56 - 53100 Siena, (Italy) 

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