Reducing the Subjectivity of Gene Expression Data Clustering Based on Spatial Contiguity Analysis

  • Hui Yi
  • Xiaofeng Song
  • Bin Jiang
  • Yufang Liu
Conference paper

DOI: 10.1007/978-3-642-27157-1_13

Part of the Communications in Computer and Information Science book series (CCIS, volume 258)
Cite this paper as:
Yi H., Song X., Jiang B., Liu Y. (2011) Reducing the Subjectivity of Gene Expression Data Clustering Based on Spatial Contiguity Analysis. In: Kim T. et al. (eds) Database Theory and Application, Bio-Science and Bio-Technology. Communications in Computer and Information Science, vol 258. Springer, Berlin, Heidelberg

Abstract

Clustering, which has been widely used as a forecasting tool for gene expression data, remains problematic at a very deep level: different initial points of clustering lead to different processes of convergence. However, the setting of initial points is mainly dependent on the judgments of experimenters. This subjectivity brings problems, including local minima and an extra computing consumption when bad initial points are selected. Hence, spatial contiguity analysis has been implemented to reduce the subjectivity of clustering. Data points near the cluster centroids are selected as initial points in this paper. This accelerates the process of convergence, and avoids the local minima. The proposed approach has been validated on benchmark datasets, and satisfactory results have been obtained.

Keywords

Clustering Gene expression Subjectivity Initial points setting 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hui Yi
    • 1
  • Xiaofeng Song
    • 1
  • Bin Jiang
    • 1
  • Yufang Liu
    • 1
    • 2
  1. 1.Department of Biomedical EngineeringNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Guodian Environment Protection Research InstituteNanjingChina

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