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Accession Number:

AD1157337

Title:

Multi-View Clustering of Social-Based Data

Author(s):

• Cruickshank, Iain J

Author Organization(s):

• CARNEGIE INST OF TECH PITTSBURGH PA

Report Date:

2020-07-01

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 Type:

Conference:

Journal:

Pages:

221

File Size:

6.24MB

Contracts:

Grants:

Descriptors:

Identifiers:

• computational social science
• social network analysis
• graph learning
• community detection
• multi-view data
• multi-modal data
• cluster ensembling
• malware analysis

SubjectCategory:

• Behavioral and Social Sciences

Communities of Interest:

• Cyber

Distribution Statement:

Approved For Public Release

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