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We propose a novel and easy method to implement dissimilarity measure for functional data clustering based on smoothing splines and smoothing parameter commutation. This method handles data observed at regular or irregular time points in the same way. We measure the dissimilarity between subjects based on varying curve estimates with pairwise commutation of smoothing parameters. The intuition is that smoothing parameters of smoothing splines reflect the inverse of the signal-to-noise ratios and that when applying an identical smoothing parameter the smoothed curves for two similar subjects are expected to be close. Our method takes into account the estimation uncertainty using smoothing parameter commutation and is not strongly affected by outliers. It can also be used for outlier detection. The effectiveness of our proposal is shown by simulations comparing it to other dissimilarity measures and by a real application to methadone dosage maintenance levels.<\/jats:p>","DOI":"10.1177\/0962280217710050","type":"journal-article","created":{"date-parts":[[2017,5,24]],"date-time":"2017-05-24T03:35:22Z","timestamp":1495596922000},"page":"3492-3504","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":7,"title":["Dissimilarity for functional data clustering based on smoothing parameter commutation"],"prefix":"10.1177","volume":"27","author":[{"given":"ShengLi","family":"Tzeng","sequence":"first","affiliation":[{"name":"Department of Public Health, China Medical University, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christian","family":"Hennig","sequence":"additional","affiliation":[{"name":"Department of Statistical Science, University College London, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Fen","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Public Health, China Medical University, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chien-Ju","family":"Lin","sequence":"additional","affiliation":[{"name":"MRC Biostatistics Unit, University of Cambridge, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2017,5,24]]},"reference":[{"key":"bibr1-0962280217710050","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-28349-8_2"},{"key":"bibr2-0962280217710050","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2012.12.008"},{"key":"bibr3-0962280217710050","doi-asserted-by":"publisher","DOI":"10.1002\/widm.53"},{"key":"bibr4-0962280217710050","doi-asserted-by":"publisher","DOI":"10.1201\/b19706"},{"key":"bibr5-0962280217710050","volume-title":"Nonparametric functional data analysis: theory and practice","author":"Ferraty F","year":"2006"},{"key":"bibr6-0962280217710050","doi-asserted-by":"publisher","DOI":"10.1080\/10629360600880684"},{"key":"bibr7-0962280217710050","doi-asserted-by":"publisher","DOI":"10.1080\/03610910903168603"},{"key":"bibr8-0962280217710050","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2006.04.035"},{"key":"bibr9-0962280217710050","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2009.02.015"},{"key":"bibr10-0962280217710050","doi-asserted-by":"publisher","DOI":"10.1080\/00949659608811737"},{"key":"bibr11-0962280217710050","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9469.00350"},{"key":"bibr12-0962280217710050","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/87.3.587"},{"key":"bibr13-0962280217710050","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2005.01.025"},{"key":"bibr14-0962280217710050","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/91.1.195"},{"key":"bibr15-0962280217710050","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2005.04.012"},{"key":"bibr16-0962280217710050","author":"de Lucas DC","journal-title":"Classification techniques for time series and functional data"},{"key":"bibr17-0962280217710050","unstructured":"Brandmaier AM.\n                      Permutation distribution clustering and structural equation model trees\n                      . 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