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Citation - Scopus: 1
Annealing-Based Model-Free Expectation Maximisation for Multi-Colour Flow Cytometry Data Clustering
(Inderscience Enterprises Ltd., 2016) Köktürk, Başak Esin; Karaçalı, Bilge
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.

Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği