Evaluation Of Hierarchical Clustering Algorithms As Classifiers Of Hypertensive Blood Pressure And Heart Rate Recordings
Price
Free (open access)
Transaction
Volume
4
Pages
10
Published
1997
Size
964 kb
Paper DOI
10.2495/BIO970231
Copyright
WIT Press
Author(s)
A.N. Kastania & M.P. Bekakos
Abstract
One of the biggest problems in hierarchical cluster analysis is validity of the classification results since various methods are available. This study evaluates hierarchical clustering algorithms as classifiers of 24h hypertensive blood pressure and heart rate recordings based on an algorithm designed and developed for this purpose. It also attempts to provide a comparison of the derived clusters with the results of a pattern recognition algorithm for biomedical waveform analysis in order to clarify the limitations on the use of hierarchical clustering methods for the classification of biomedical waveforms. 1 Introduction Cluster analysis is a tool of exploratory data analysis that
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