Paper Info
Title
A New MI-Based Visualization Aided Validation Index for Mining Big Longitudinal Web Trial Data.
Abstract
Web-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 (VOS), 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 & Beni (XB) index. Through optimizing the view angles of 3D projections using Sammon mapping, the optimal 2D projection-guided MIVOOS is obtained to better visualize and verify the patterns in conjunction with trajectory patterns. Compared to 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.
Year
DOI
Venue
2016
10.1109/ACCESS.2016.2569074
IEEE Access
Keywords
Field
DocType
Multiple imputation,clustering validation,longitudinal web trial data,pattern recognition,visualization
Sammon mapping,Data mining,Fuzzy clustering,Data visualization,Visualization,Computer science,Robustness (computer science),Unsupervised learning,Artificial intelligence,Missing data,Imputation (statistics),Machine learning
Journal
Volume
Issue
ISSN
4
99
2169-3536
Citations 
PageRank 
References 
4
0.40
7
Authors
3
Name
Order
Citations
PageRank
Z. Zhang12308198.54
Hua Fang234332.48
Honggang Wang31365124.06