liunian-Jay/ICMVC
[AAAI 2024] ICMVC: Incomplete Contrastive Multi-View Clustering with High-confidence Guiding
This is a machine learning tool designed for researchers and data scientists working with complex datasets that have multiple 'views' or modalities, such as images with associated text descriptions, but where some of that data is missing. It helps you group similar items together even when parts of the information are incomplete. You input your multi-view dataset, and it outputs clusters of your data, helping you find hidden patterns or relationships.
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Use this if you need to perform clustering on datasets where each item has multiple types of data associated with it (e.g., visual, textual, numerical), but some of these data points are missing for various items.
Not ideal if your dataset is complete with no missing information across its multiple views, or if you only have a single 'view' of your data.
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Python
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Jul 22, 2024
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