Abstract
Data clustering is a major data mining technique and has been shown to be useful in a wide variety of domains, including medical and biological statistical data analysis. A non trivial application of cluster analysis occurs in the identification of different subpopulations of particles in large-sized heterogeneous flow-cytometric data. Mixture-model based clustering has been several times applied in the past to medical and biological data analysis; to our knowledge, however, non of these applications was involved with flow-cytometric data. We claim, that utilizing the probabilities mixture model offers several advantages compared to other proposed flow-cytometric data clustering approaches. We apply this model in order to gate lymphocytes in peripheral blood, which is a necessary first-step procedure when dealing with various hematological diseases diagnoses, such as lymphocytic leukemias and lymphoma.
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Lakoumentas, J., Drakos, J., Karakantza, M., Zoumbos, N., Nikiforidis, G., Sakellaropoulos, G. (2006). The Probabilities Mixture Model for Clustering Flow-Cytometric Data: An Application to Gating Lymphocytes in Peripheral Blood. In: Maglaveras, N., Chouvarda, I., Koutkias, V., Brause, R. (eds) Biological and Medical Data Analysis. ISBMDA 2006. Lecture Notes in Computer Science(), vol 4345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11946465_14
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DOI: https://doi.org/10.1007/11946465_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68063-5
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