Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5494
Title: Annealing-based model-free expectation maximisation for multi-colour flow cytometry data clustering
Authors: Köktürk, Başak Esin
Karaçalı, Bilge
Keywords: Clustering
Data mining
Flow cytometry data analysis
Bioinformatics
Simulated Annealing algorithm
Issue Date: 2016
Publisher: Inderscience Enterprises Ltd.
Source: Köktürk, B. E., and Karaçalı, B. (2016). Annealing-based model-free expectation maximisation for multi-colour flow cytometry data clustering. International Journal of Data Mining and Bioinformatics, 14(1), 86-99. doi:10.1504/IJDMB.2016.073365
Abstract: This paper proposes an optimised model-free expectation maximisation 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 carriedout using a model-free expectation maximisation scheme that exploits the posterior probability computation capability of the quasi-supervised learningalgorithm subjected to a line-search optimisation over the reference set size parameter analogous to a simulated annealing approach. The divisions arecontinued until a division cost exceeds an adaptively determined limit. Experiment results on synthetic as well as real multi-colour flow cytometrydatasets showed that the proposed method can accurately capture the prominent clusters without requiring any prior knowledge on the number of clusters ortheir distribution models.
URI: http://doi.org/10.1504/IJDMB.2016.073365
http://hdl.handle.net/11147/5494
ISSN: 1748-5673
1748-5673
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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