Active Data Clustering

NeurIPS 1997 pp. 528-534

/neurips/1997/hofmann1997neurips-active/

Abstract

Active data clustering is a novel technique for clustering of proxim(cid:173) ity data which utilizes principles from sequential experiment design in order to interleave data generation and data analysis. The pro(cid:173) posed active data sampling strategy is based on the expected value of information, a concept rooting in statistical decision theory. This is considered to be an important step towards the analysis of large(cid:173) scale data sets, because it offers a way to overcome the inherent data sparseness of proximity data. '''Ie present applications to unsu(cid:173) pervised texture segmentation in computer vision and information retrieval in document databases.

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Text

Hofmann and Buhmann. "Active Data Clustering." Neural Information Processing Systems, 1997.

Markdown

[Hofmann and Buhmann. "Active Data Clustering." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/hofmann1997neurips-active/)

BibTeX

@inproceedings{hofmann1997neurips-active,
  title     = {{Active Data Clustering}},
  author    = {Hofmann, Thomas and Buhmann, Joachim M.},
  booktitle = {Neural Information Processing Systems},
  year      = {1997},
  pages     = {528-534},
  url       = {https://mlanthology.org/neurips/1997/hofmann1997neurips-active/}
}