A Framework for Deep Constrained Clustering - Algorithms and Advances
| Autor: | Ian Davidson, Sugato Basu, Hongjing Zhang |
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| Rok vydání: | 2020 |
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Computer science business.industry Deep learning media_common.quotation_subject Constrained clustering 020207 software engineering 02 engineering and technology Mixture model Field (computer science) Spectral clustering 0202 electrical engineering electronic engineering information engineering Domain knowledge 020201 artificial intelligence & image processing Artificial intelligence Cluster analysis business Algorithm media_common |
| Zdroj: | Machine Learning and Knowledge Discovery in Databases ISBN: 9783030461492 ECML/PKDD (1) |
| DOI: | 10.1007/978-3-030-46150-8_4 |
| Popis: | The area of constrained clustering has been extensively explored by researchers and used by practitioners. Constrained clustering formulations exist for popular algorithms such as k-means, mixture models, and spectral clustering but have several limitations. A fundamental strength of deep learning is its flexibility, and here we explore a deep learning framework for constrained clustering and in particular explore how it can extend the field of constrained clustering. We show that our framework can not only handle standard together/apart constraints (without the well documented negative effects reported earlier) generated from labeled side information but more complex constraints generated from new types of side information such as continuous values and high-level domain knowledge. (Source code available at: http://github.com/blueocean92/deep_constrained_clustering) |
| Databáze: | OpenAIRE |
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