Publication type: Conference paper
Type of review: Peer review (abstract)
Title: The challenge of clustering flow cytometry data from phytoplankton in Lakes
Authors: Glüge, Stefan
Pomati, Francesco
Albert, Carlo
Kauf, Peter
Ott, Thomas
DOI: 10.1007/978-3-319-08672-9_45
Proceedings: Nonlinear Dynamics of Electronic Systems 22nd International Conference, NDES 2014, Albena, Bulgaria, July 4-6, 2014. Proceedings
Editors of the parent work: Mladenov, V.M.
Ivanov, P.C.
Page(s): 379
Pages to: 386
Conference details: NDES 2014, 22nd Nonlinear Dynamics of Electronic Systems Conference, Albena, Bulgaria, 4-6 July 2014
Issue Date: 2014
Series: Communications in Computer and Information Science
Series volume: 438
Publisher / Ed. Institution: Springer
Publisher / Ed. Institution: Cham
ISBN: 978-3-319-08671-2
ISSN: 1865-0929
Language: German
Subjects: Phytoplankton; Flow Cytometry; Clustering; Sequential Superparamagnetic Clustering
Subject (DDC): 004: Computer science
Abstract: ClusteringFlow cytometry (FC) devices count and measure cells in fluids in an automated procedure. In this paper we present our work in progress on the clustering of FC data. We compare standard clustering algorithms such as K-means, Ward’s clustering, etc., to the more advanced approach of sequential superparamagnetic clustering (SSC). We found Ward’s hierarchical clustering to perform best regarding internal cluster validation measures, while SSC yielded the best results based on the visual inspection of the clustering results.
Fulltext version: Published version
License (according to publishing contract): Licence according to publishing contract
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Computational Life Sciences (ICLS)
Appears in collections:Publikationen Life Sciences und Facility Management

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