WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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Institution Author: search.filters.institutionAuthor.Karaçalı, BilgePublisher: search.filters.publisher.IEEE

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    Citation - Scopus: 2
    Model-Free Expectation Maximization for Divisive Hierarchical Clustering of Multicolor Flow Cytometry Data
    (IEEE, 2014) Köktürk, Başak Esin; Karaçalı, Bilge; Karaçalı, Bilge; 03.05. Department of Electrical and Electronics Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    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.