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A Novel Stability Based Feature Selection Framework for k-means Clustering

  • Dimitrios Mavroeidis
  • Elena Marchiori
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6912)

Abstract

Stability of a learning algorithm with respect to small input perturbations is an important property, as it implies the derived models to be robust with respect to the presence of noisy features and/or data sample fluctuations. In this paper we explore the effect of stability optimization in the standard feature selection process for the continuous (PCA-based) k-means clustering problem. Interestingly, we derive that stability maximization naturally introduces a tradeoff between cluster separation and variance, leading to the selection of features that have a high cluster separation index that is not artificially inflated by the feature’s variance. The proposed algorithmic setup is based on a Sparse PCA approach, that selects the features that maximize stability in a greedy fashion. In our study, we also analyze several properties of Sparse PCA relevant to stability that promote Sparse PCA as a viable feature selection mechanism for clustering. The practical relevance of the proposed method is demonstrated in the context of cancer research, where we consider the problem of detecting potential tumor biomarkers using microarray gene expression data. The application of our method to a leukemia dataset shows that the tradeoff between cluster separation and variance leads to the selection of features corresponding to important biomarker genes. Some of them have relative low variance and are not detected without the direct optimization of stability in Sparse PCA based k-means.

Keywords

Feature Selection Feature Subset Normalize Mutual Information Feature Selection Algorithm Cluster Separation 
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 2011

Authors and Affiliations

  • Dimitrios Mavroeidis
    • 1
  • Elena Marchiori
    • 1
  1. 1.Institute for Computing and Information SciencesRadboud UniversityNijmegenThe Netherlands

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