Application of Clustering Techniques to the Classification of Marine Phytoplankton

An introduction to classification methods is given with sections on Flow Cytometry, Clustering and so on. This includes a literature survey on research into fuzzy clustering algorithms, with a section specifically related to Flow Cytometry. Details are given about the data sets and the software used, and the clustering algorithms investigated. The flow cytometry data was collected for marine phytoplankton. Two groups of data are used, one containing species that overlapped each other, and one containing independent species (non-overlapped). Ten clusters of 1000 records each are collated for each group, each record comprising of seven variables. Six clustering algorithms (Fuzzy K-Means, Adaptive Distances, Fuzzy K-Means, Generalised Distances, Maximum Likelihood, Minimum Total Volume, and Sum of all Normalised Determinants) are used to cluster the flow cytometry data. The results are compared for each group of data, for each algorithm, based on the number of clusters produced and the relationships of the phytoplankton species placed in each cluster. Conclusions are drawn about the suitability of each algorithm to cluster phytoplankton flow cytometry data, and a discussion follows on some flow cytometry data issues. Various potential algorithms that could be investigated in future research are discussed.

Name dc:type.qualificationname

Master's Thesis

Level dc:type.qualificationlevel

Student thesis

Year dc:date.issued

2004

Author dc:creator

• Hardy, Samantha Ann

Language dc:language

eng

Identifier

oai:pure.atira.dk:studenttheses/cb560e67-9290-46ec-8e3b-7a5c26c88911

OAI identifier oai:identifier

oai:pure.atira.dk:studenttheses/cb560e67-9290-46ec-8e3b-7a5c26c88911