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Basics of Multivariate Analysis in Neuroimaging Data
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Multi-View Cluster Analysis with Incomplete Data to Understand Treatment Effects.

Guoqing Chao1, Jiangwen Sun2, Jin Lu1

  • 1Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.

Information Sciences
|September 1, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-view clustering method to effectively handle missing data by using an indicator matrix. This approach improves data utilization and accuracy in complex datasets, outperforming existing methods.

Keywords:
co-clusteringgranular computingheroin pharmacotherapymissing valuemulti-view data analysis

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Area of Science:

  • Data Science
  • Machine Learning
  • Computational Statistics

Background:

  • Multi-view cluster analysis partitions subjects into consistent clusters across different data views.
  • Existing methods struggle with incomplete multi-view data, especially mixed missing patterns.
  • Missing data is a common challenge in real-world datasets.

Purpose of the Study:

  • To propose an enhanced multi-view co-clustering formulation for handling missing data.
  • To develop a method less sensitive to imputation uncertainty and data removal.
  • To enable analysis of datasets with missing values in any view or entire views.

Main Methods:

  • Introduced an indicator matrix to identify observed data entries.
  • Assessed cluster validity solely on observed entries, not imputed or removed data.
  • Developed a robust multi-view clustering approach for incomplete datasets.

Main Results:

  • The proposed method effectively handles missing data in multi-view clustering.
  • It outperforms traditional methods that remove or impute missing values.
  • Demonstrated superior performance in simulations and a real-world heroin dependence study.

Conclusions:

  • The enhanced formulation provides a robust solution for multi-view clustering with missing data.
  • The method successfully identified patient subgroups and predictive baseline features in a treatment study.
  • This approach expands the applicability of multi-view clustering to complex, real-world datasets.