Abstract
Many techniques have already been suggested for handling and analyzing the large and high-dimensional data sets produced by newly developed gene expression experiments. These techniques include supervised classification and unsupervised agglomerative or hierarchical clustering techniques. Here, we present an alternative approach that does not make assumption on the shape, size and volumes of the clusters. The technique is based on the estimation of the probability density function (pdf). Once the pdf is estimated, with the Parzen technique (with the right amount of smoothing), the parameter space is partitioned according to methods inherited from image processing, namely the skeleton by influence zones and the watershed. We show some advantages of this suggested approach.
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Bonnet, N., Herbin, M., Cutrona, J., Zahm, JM. (2002). A New Clustering Approach, Based on the Estimation of the Probability Density Function, for Gene Expression Data. In: Jajuga, K., Sokołowski, A., Bock, HH. (eds) Classification, Clustering, and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56181-8_3
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DOI: https://doi.org/10.1007/978-3-642-56181-8_3
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