A Theoretical Framework for Supporting Clustering Validation via Non-Negative-Matrix-Factorization Trace Sequences Over Probabilistic Spaces.
Alfredo CuzzocreaPau FigueraMojtaba HajianPablo García BringasPublished in: DASC/PiCom/CBDCom/CyberSciTech (2023)
Keyphrases
- theoretical framework
- negative matrix factorization
- document clustering
- nonnegative matrix factorization
- spectral clustering
- probabilistic latent semantic analysis
- matrix factorization
- k means
- clustering method
- sparse representation
- principal component analysis
- theoretical foundation
- clustering algorithm
- information theoretic
- unsupervised feature selection
- generative model
- bayesian networks
- data clustering
- tensor factorization
- cluster analysis
- high dimensional data
- text mining
- probabilistic model
- knowledge base
- document collections
- unsupervised learning
- feature selection
- machine learning