Towards an Internal Evaluation Measure for Arbitrarily Oriented Subspace Clustering.
Daniyal KazempourPeer KrögerThomas SeidlPublished in: ICDM (Workshops) (2020)
Keyphrases
- subspace clusters
- arbitrarily oriented
- evaluation measures
- subspace clustering
- precision and recall
- high dimensional data
- learning to rank
- clustering method
- clustering algorithm
- high dimensional
- retrieval systems
- high dimensionality
- ranked list
- machine learning
- benchmark datasets
- feature extraction
- learning algorithm