Efficient scalar quantization of exponential and Laplacian random variables
IEEE Transactions on Information Theory
Dissolution point and isolation robustness: Robustness criteria for general cluster analysis methods
Journal of Multivariate Analysis
Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART II
The Depth Problem: Identifying the Most Representative Units in a Data Group
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Clustering and classification are important tasks for the analysis of microarray gone expression data. Classification of tissue samples can be a valuable diagnostic tool for diseases such as cancer. Clustering samples or experirments may lead to the discovery of subclasses of diseases. Clustering genes can help identify groups of genes that respond similarly to a set of experimental conditions. We also need validation tools for clustering and classification. Here, we focus on the identification of outliers--units that may have been misallocated, or mislabeled, or are not representative of the classes or clusters.We present two new methods: DDclust and DDclass, for clustering and classification. These non-parametric methods are based on the intuitively simple concept of data depth. We apply the methods to several gene expression and simulated data sets. We also discuss a convenient visualization and validation tool--the relative data depth plot.