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Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial
Data with Missing Values in eHealth
#MMPMID27126063
Zhang Z
; Fang H
; Wang H
J Med Syst
2016[Jun]; 40
(6
): 146
PMID27126063
show ga
Web-delivered trials are an important component in eHealth services. These
trials, mostly behavior-based, generate big heterogeneous data that are
longitudinal, high dimensional with missing values. Unsupervised learning methods
have been widely applied in this area, however, validating the optimal number of
clusters has been challenging. Built upon our multiple imputation (MI) based
fuzzy clustering, MIfuzzy, we proposed a new multiple imputation based validation
(MIV) framework and corresponding MIV algorithms for clustering big longitudinal
eHealth data with missing values, more generally for fuzzy-logic based clustering
methods. Specifically, we detect the optimal number of clusters by auto-searching
and -synthesizing a suite of MI-based validation methods and indices, including
conventional (bootstrap or cross-validation based) and emerging
(modularity-based) validation indices for general clustering methods as well as
the specific one (Xie and Beni) for fuzzy clustering. The MIV performance was
demonstrated on a big longitudinal dataset from a real web-delivered trial and
using simulation. The results indicate MI-based Xie and Beni index for
fuzzy-clustering are more appropriate for detecting the optimal number of
clusters for such complex data. The MIV concept and algorithms could be easily
adapted to different types of clustering that could process big incomplete
longitudinal trial data in eHealth services.
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