A clustering framework based on subjective and objective validity criteria

@article{Halkidi2008ACF,
  title={A clustering framework based on subjective and objective validity criteria},
  author={Maria Halkidi and Dimitrios Gunopulos and Michalis Vazirgiannis and Nitin Kumar and Carlotta Domeniconi},
  journal={TKDD},
  year={2008},
  volume={1},
  pages={4:1-4:25}
}
Clustering, as an unsupervised learning process is a challenging problem, especially in cases of high-dimensional datasets. Clustering result quality can benefit from user constraints and objective validity assessment. In this article, we propose a semisupervised framework for learning the weighted Euclidean subspace, where the best clustering can be… CONTINUE READING