Clustering and a Dissimilarity Measure for Methadone Dosage Time Series (original) (raw)

Abstract

In this work we analyse data for 314 participants of a methadone study over 180 days. Dosages in milligram were converted for better interpretability to seven categories in which six categories have an ordinal scale for representing dosages and one category for missing dosages. We develop a dissimilarity measure and cluster the time series using “partitioning around medoids” (PAM). The dissimilarity measure is based on assessing the interpretative dissimilarity between categories. It quantifies the structure of the categories which is partly categorical, partly ordinal and also involves quantitative information. The principle behind the measure can be used for other applications as well, in which there is more information about the meaning of categories than just that they are “ordinal” or “categorical”.

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Authors and Affiliations

1. Department of Statistical Science, University College London, London, UK
   Chien-Ju Lin & Christian Hennig
2. MRC Biostatistics Unit, Cambridge, UK
   Chien-Ju Lin
3. Department of Psychiatry, China Medical University Hospital, Taichung City, Taiwan, ROC
   Chieh-Liang Huang

Authors

1. Chien-Ju Lin
2. Christian Hennig
3. Chieh-Liang Huang

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Correspondence toChien-Ju Lin .

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Editors and Affiliations

1. Jacobs University Bremen , Bremen, Germany
   Adalbert F.X. Wilhelm
2. Universität Ulm, Institute of Medical Systems Biology Universität Ulm, Ulm, Baden-Württemberg, Germany
   Hans A. Kestler

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© 2016 Springer International Publishing Switzerland

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Lin, CJ., Hennig, C., Huang, CL. (2016). Clustering and a Dissimilarity Measure for Methadone Dosage Time Series. In: Wilhelm, A., Kestler, H. (eds) Analysis of Large and Complex Data. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-25226-1\_3

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• DOI: https://doi.org/10.1007/978-3-319-25226-1\_3
• Published: 04 August 2016
• Publisher Name: Springer, Cham
• Print ISBN: 978-3-319-25224-7
• Online ISBN: 978-3-319-25226-1
• eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)Springer Nature Proceedings excluding Computer Science

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