An objective evaluation criterion for clustering

  title={An objective evaluation criterion for clustering},
  author={Arindam Banerjee and John Langford},
We propose and test an objective criterion for evaluation of clustering performance: How well does a clustering algorithm run on unlabeled data aid a classification algorithm? The accuracy is quantified using the PAC-MDL bound [3] in a semisupervised setting. Clustering algorithms which naturally separate the data according to (hidden) labels with a small number of clusters perform well. A simple extension of the argument leads to an objective model selection method. Experimental results on… CONTINUE READING
Highly Cited
This paper has 40 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 27 extracted citations

Bregman Bubble Clustering: A Robust, Scalable Framework for Locating Multiple, Dense Regions in Data

Sixth International Conference on Data Mining (ICDM'06) • 2006
View 2 Excerpts
Highly Influenced

Geriatric group analysis by clustering non-linearly embedded multi-sensor data

2018 Innovations in Intelligent Systems and Applications (INISTA) • 2018
View 2 Excerpts

Similar Papers

Loading similar papers…