A New MI-Based Visualization Aided Validation Index for Mining Big Longitudinal Web Trial DataQuantitative Health Sciences Publications and Presentations
UMMS AffiliationDepartment of Quantitative Health Sciences
Document TypeArticle Postprint
AbstractWeb-delivered clinical trials generate big complex data. To help untangle the heterogeneity of treatment effects, unsupervised learning methods have been widely applied. However, identifying valid patterns is a priority but challenging issue for these methods. This paper, built upon our previous research on multiple imputation (MI)-based fuzzy clustering and validation, proposes a new MI-based Visualization-aided validation index (MIVOOS) to determine the optimal number of clusters for big incomplete longitudinal Web-trial data with inflated zeros. Different from a recently developed fuzzy clustering validation index, MIVOOS uses a more suitable overlap and separation measures for Web-trial data but does not depend on the choice of fuzzifiers as the widely used Xie and Beni (XB) index. Through optimizing the view angles of 3-D projections using Sammon mapping, the optimal 2-D projection-guided MIVOOS is obtained to better visualize and verify the patterns in conjunction with trajectory patterns. Compared with XB and VOS, our newly proposed MIVOOS shows its robustness in validating big Web-trial data under different missing data mechanisms using real and simulated Web-trial data.
- UMCCTS funding,
- multiple imputation,
- clustering validation,
- pattern recognition,
- longitudinal web trial data
DOI of Published Version10.1109/ACCESS.2016.2569074.
SourceZhang Z, Fang H, Wang H. A New MI-based Visualization Aided Validation Index for Mining Big Longitudinal Web Trial Data. IEEE Access. 2016;4:2272-2280. First published online 2016 May 16. The final publication is available at IEEE Xplore via http://dx.doi.org/10.1109/ACCESS.2016.2569074.
Citation InformationZhaoyang Zhang, Hua (Julia) Fang and Honggang Wang. "A New MI-Based Visualization Aided Validation Index for Mining Big Longitudinal Web Trial Data" Vol. 4 (2016)
Available at: http://works.bepress.com/hua_fang/40/