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Article
MIFuzzy Clustering for Incomplete Longitudinal Data in Smart Health
UMass Center for Clinical and Translational Science Supported Publications
  • Hua (Julia) Fang, University of Massachusetts Medical School
UMMS Affiliation
Department of Quantitative Health Sciences
Publication Date
6-1-2017
Document Type
Article
Abstract
Missing data are common in longitudinal observational and randomized controlled trials in smart health studies. Multiple-imputation based fuzzy clustering is an emerging non-parametric soft computing method, used for either semi-supervised or unsupervised learning. Multiple imputation (MI) has been widely-used in missing data analyses, but has not yet been scrutinized for unsupervised learning methods, although they are important for explaining the heterogeneity of treatment effects. Built upon our previous work on MIfuzzy clustering, this paper introduces the MIFuzzy concepts and performance, theoretically, empirically and numerically demonstrate how MI-based approach can reduce the uncertainty of clustering accuracy in comparison to non- and single-imputation based clustering approach. This paper advances our understanding of the utility and strength of MIFuzzy clustering approach to processing incomplete longitudinal behavioral intervention data.
Keywords
  • UMCCTS funding,
  • longitudinal data,
  • Missing values,
  • Fuzzy clustering,
  • Multiple imputation,
  • MIFuzzy
DOI of Published Version
10.1016/j.smhl.2017.04.002
Source
Smart Health (Amst). 2017 Jun;1-2:50-65. doi: 10.1016/j.smhl.2017.04.002. Epub 2017 Apr 27. Link to article on publisher's site
Related Resources
Link to Article in PubMed
PubMed ID
28993813
Citation Information
Hua (Julia) Fang. "MIFuzzy Clustering for Incomplete Longitudinal Data in Smart Health" Vol. 1-2 (2017) ISSN: 2352-6483 (Print)
Available at: http://works.bepress.com/hua_fang/47/