Int. J. of Data Mining and Bioinformatics   »   2016 Vol.14, No.1

 

 

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

 

Authors: Başak Esin Köktürk; Bilge Karaçalı

 

Addresses:
Department of Electrical and Electronics Engineering, İzmir Institute of Technology, İzmir, Turkey
Department of Electrical and Electronics Engineering, İzmir Institute of Technology, İzmir, Turkey

 

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 carried out using a model-free expectation maximisation scheme that exploits the posterior probability computation capability of the quasi-supervised learning algorithm subjected to a line-search optimisation over the reference set size parameter analogous to a simulated annealing approach. The divisions are continued until a division cost exceeds an adaptively determined limit. Experiment results on synthetic as well as real multi-colour flow cytometry datasets showed that the proposed method can accurately capture the prominent clusters without requiring any prior knowledge on the number of clusters or their distribution models.

 

Keywords: expectation maximisation; quasi-supervised learning; data clustering; gating; multi-colour flow cytometry data; simulated annealing; data analysis; bioinformatics; high-dimensional datasets; prominent clusters.

 

DOI: 10.1504/IJDMB.2016.073365

 

Int. J. of Data Mining and Bioinformatics, 2016 Vol.14, No.1, pp.86 - 99

 

Submission date: 28 Apr 2015
Date of acceptance: 15 May 2015
Available online: 30 Nov 2015

 

 

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