Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/9962
Title: Model-free expectation maximization for divisive hierarchical clustering of multicolor flow cytometry data
Authors: Köktürk, Başak Esin
Karaçalı, Bilge
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers Inc.
Series/Report no.: IEEE International Conference on Bioinformatics and Biomedicine-BIBM
Abstract: This paper proposes a new method for automated clustering of high dimensional datasets. The method is based on a recursive binary division strategy that successively divides an original dataset into distinct clusters. Each binary division is carried out using a model-free expectation maximization scheme that exploits the posterior probability computation capability of the quasi-supervised learning algorithm. The divisions are carried out until a division cost exceeds an adaptively determined limit. Experiment results on synthetic as well as real multi-color flow cytometry datasets showed that the proposed method can accurately capture the prominent clusters without requiring any knowledge on the number of clusters or their distribution models.
Description: IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)
URI: https://hdl.handle.net/11147/9962
ISBN: 978-1-4799-5669-2
ISSN: 2156-1125
2156-1133
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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