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Annealing-based model-free expectation maximisation for multi-colour flow cytometry data clustering

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info:eu-repo/semantics/openAccess

Date

2016

Author

Köktürk, Başak Esin
Karaçalı, Bilge

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Citation

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.

Source

International Journal of Data Mining and Bioinformatics

Volume

14

Issue

1

URI

http://doi.org/10.1504/IJDMB.2016.073365
http://hdl.handle.net/11147/5494

Collections

  • Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği [351]
  • Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection [4673]
  • WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection [4803]



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