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    eFCM: An Enhanced Fuzzy C-Means Algorithm for Longitudinal Intervention Data

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    Authors
    Gurugubelli, Venkata Sukumar
    Li, Zhouzhou
    Wang, Honggang
    Fang, Hua (Julia)
    UMass Chan Affiliations
    Department of Quantitative Health Sciences
    Document Type
    Journal Article
    Publication Date
    2018-03-01
    Keywords
    UMCCTS funding
    Computer Sciences
    Longitudinal Data Analysis and Time Series
    Translational Medical Research
    
    Metadata
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    Link to Full Text
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428443/
    Abstract
    Clustering methods become increasingly important in analyzing heterogeneity of treatment effects, especially in longitudinal behavioral intervention studies. Methods such as K-means and Fuzzy C-means (FCM) have been widely endorsed to identify distinct groups of different types of data. Build upon our MIFuzzy [1], our goal is to concurrently handle multiple methodological issues in studying high dimensional longitudinal intervention data with missing values. Particularly, this paper focuses on the initialization issue of FCM and proposes a new initialization method to overcome the local optimal problem and decrease the convergence time in handling high-dimensional data with missing values for overlapping clusters. Based on the idea of K-means++ [9], we proposed an enhanced Fuzzy C-means clustering (eFCM) and incorporated it into our MIFuzzy. This method was evaluated using real longitudinal intervention data, classic and generic datasets. Compared to conventional FCM, our findings indicate eFCM can improve computational efficiency and avoid the local optimization.
    Source

    Int Conf Comput Netw Commun. 2018 Mar;2018:912-916. doi: 10.1109/ICCNC.2018.8390419. Epub 2018 Jun 21. Link to article on publisher's site

    DOI
    10.1109/ICCNC.2018.8390419
    Permanent Link to this Item
    http://hdl.handle.net/20.500.14038/50344
    PubMed ID
    30906794
    Related Resources

    Link to Article in PubMed

    ae974a485f413a2113503eed53cd6c53
    10.1109/ICCNC.2018.8390419
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