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Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth

  • Zhaoyang Zhang
  • Hua FangEmail author
  • Honggang Wang
Systems-Level Quality Improvement
Part of the following topical collections:
  1. Advances in Big-Data based mHealth Theories and Applications

Abstract

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.

Keywords

Big data Validation Multiple imputation Fuzzy clustering Missing data Longitudinal trial 

Notes

Acknowledgment

This research was supported by NIH grant R01 DA033323, 1UL1RR031982-01 Pilot Project to Dr. Fang. We thank Dr. Thomas Huston for providing their longitudinal web-delivered QuitPrimo trial data. This work was partially supported by the National Science Foundation through awards IIS#1401711, ECCS#1407882.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Quantitative Health ScienceUniversity of Massachusetts Medical SchoolWorcesterUSA
  2. 2.Department of Electrical and Computer EngineeringUniversity of Massachusetts DartmouthNorth DartmouthUSA

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