Multi-View Clustering of Social-Based Data
Abstract
Real-world, social phenomena produce various types of data, like explicit networks or user-emitted text. When different sets of data describe the same entities, the data is termed multi-view or multi-modal. A distinct advantage of multi-view data is that different views may better capture different aspects of the latent structure of the data. However, there are difficulties in combining that data to produce somethinglike a clustering of the data. Multi-view clustering techniques, primarily developed for image or biological use cases or network only use cases, have typically not been used for clustering social-based use cases. I investigate the use of multi-view clustering on various social-based, multi-view data sets, and propose new techniques for multi-view clustering of social-based data.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 2020
- Accession Number
- AD1157337
Entities
People
- Iain J. Cruickshank
Organizations
- Carnegie Institute of Technology