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Related Experiment Videos

Robust clustering in high dimensional data using statistical depths.

Yuanyuan Ding1, Xin Dang, Hanxiang Peng

  • 1Computer & Information Science Department, The University of Mississippi, University, MS, USA. yding@olemiss.edu

BMC Bioinformatics
|December 6, 2007
PubMed
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A new robust divisive clustering algorithm, bisecting k-spatialMedian, excels in high-dimensional, low-sample-size data. It outperforms existing methods, offering better robustness for gene expression data analysis.

Area of Science:

  • Computational Biology
  • Data Science
  • Statistical Learning

Background:

  • Traditional mean-based clustering algorithms like bisecting k-means are not robust.
  • Componentwise median offers robustness but can be a poor center representative for high-dimensional data.
  • A need exists for robust clustering algorithms suitable for high-dimensional datasets, such as gene expression data.

Purpose of the Study:

  • To introduce a novel robust divisive clustering algorithm.
  • To address limitations of existing methods in high-dimensional and noisy datasets.
  • To improve clustering accuracy and robustness in gene expression data analysis.

Main Methods:

  • Development of the bisecting k-spatialMedian algorithm, utilizing statistical spatial depth.

Related Experiment Videos

  • Introduction of a new subcluster selection rule: Relative Average Depth.
  • Application and evaluation on real-world high-dimensional and low-sample-size (HDLSS) gene expression datasets.

    • Main Results:

    • The bisecting k-spatialMedian algorithm demonstrates superior performance compared to the componentwise-median-based bisecting k-median algorithm on HDLSS data.
    • The proposed algorithm shows favorable robustness when applied to noisy real datasets.
    • Validation through applications on two distinct HDLSS gene expression datasets.

    Conclusions:

    • Statistical data depths offer a robust and effective alternative for identifying the center of multivariate datasets.
    • The proposed bisecting k-spatialMedian algorithm is a valuable tool for robust clustering, particularly in high-dimensional settings.
    • Data depth-based clustering provides a promising approach for analyzing complex biological data.