hiroyuki-kasai/CI-GMVC

Consistency-aware and inconsistency-aware graph-based multi-view clustering

28
/ 100
Experimental

This project helps machine learning researchers and data scientists classify subjects into groups when they have multiple, possibly conflicting, perspectives on the same data. It takes in several 'views' of data, often represented as graphs, and outputs improved data clusters by explicitly accounting for both consistent and inconsistent information across those views. This is useful for anyone working with complex datasets where different features provide distinct, yet related, insights.

No commits in the last 6 months.

Use this if you are a machine learning researcher needing to improve the accuracy of your multi-view data clustering by accounting for inconsistencies between different data perspectives.

Not ideal if you are a business analyst looking for a no-code clustering solution or if your data does not inherently have multiple distinct 'views' or perspectives.

machine-learning-research multi-view-data data-clustering graph-analysis pattern-recognition
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 16 / 25
Community 7 / 25

How are scores calculated?

Stars

10

Forks

1

Language

MATLAB

License

MIT

Last pushed

Jun 08, 2021

Commits (30d)

0

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