ArticleJournal of medical systems2016
Multiple Imputation based Clustering Validation (MIV) for Big Longitudinal Trial Data with Missing Values in eHealth.
Article in Journal of medical systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Testing community-based strategies to increase hypertension screening in Nigeria: a protocol for an effectiveness-implementation hybrid type III cluster-randomized trial.Contemporary clinical trials · 2026Article
- Federated Choquet Regression with LASSO for Outcome Prediction in Multisite Longitudinal Trial Data.ACM transactions on computing for healthcare · 2026Article
- Feature Interaction Detection in Big Data Through a New Choquet Integral based Deep Neural Network.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2024Article
- Federated Fuzzy Clustering for Decentralized Incomplete Longitudinal Behavioral Data.IEEE internet of things journal · 2024Article
- Exploiting nearest neighbor data and fuzzy membership function to address missing values in classification.PeerJ. Computer science · 2024Article
- A survey on missing data in machine learning.Journal of big data · 2021Article
- Observational study protocol for evaluating control of hypertension and the effects of social determinants.BMJ open · 2019Article
- An Enhanced Visualization Method to Aid Behavioral Trajectory Pattern Recognition Infrastructure for Big Longitudinal Data.IEEE transactions on big data · 2018Article
- eFCM: An Enhanced Fuzzy C-Means Algorithm for Longitudinal Intervention Data.International Conference on Computing, Networking, and Communications : [proceedings]. International Conference on Computing, Networking and Communications · 2018Article
- MIFuzzy Clustering for Incomplete Longitudinal Data in Smart Health.Smart health (Amsterdam, Netherlands) · 2017Article
- Acculturation, Depression, and Smoking Cessation: a trajectory pattern recognition approach.Tobacco induced diseases · 2017Article
- Transforming big data into computational models for personalized medicine and health care.Dialogues in clinical neuroscience · 2016Review
- Multiple- vs Non- or Single-Imputation based Fuzzy Clustering for Incomplete Longitudinal Behavioral Intervention Data....IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies · 2016Article
- A New MI-Based Visualization Aided Validation Index for Mining Big Longitudinal Web Trial Data.IEEE access : practical innovations, open solutionsArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.