ML Anthology

ClusTR: Exploring Multivariate Cluster Correlations and Topic Trends

Luigi Di Caro, Alejandro Jaimes
ECML-PKDD 2009 pp. 722-725
doi:10.1007/978-3-642-04174-7_49 /ecmlpkdd/2009/caro2009ecmlpkdd-clustr/

Abstract

We present a demonstration of ClusTR, a highly interactive system for exploring relationships between different clusterings of a dataset and for viewing the evolution in time of topics (e.g., tags associated with objects in the dataset) within and across such clusters. In particular, ClusTR allows exploration of generic multi-dimensional, text labeled and time sensitive data.

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Cite

Text

Di Caro and Jaimes. "ClusTR: Exploring Multivariate Cluster Correlations and Topic Trends." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009. doi:10.1007/978-3-642-04174-7_49

Markdown

[Di Caro and Jaimes. "ClusTR: Exploring Multivariate Cluster Correlations and Topic Trends." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009.](https://mlanthology.org/ecmlpkdd/2009/caro2009ecmlpkdd-clustr/) doi:10.1007/978-3-642-04174-7_49

BibTeX

@inproceedings{caro2009ecmlpkdd-clustr,
  title     = {{ClusTR: Exploring Multivariate Cluster Correlations and Topic Trends}},
  author    = {Di Caro, Luigi and Jaimes, Alejandro},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2009},
  pages     = {722-725},
  doi       = {10.1007/978-3-642-04174-7_49},
  url       = {https://mlanthology.org/ecmlpkdd/2009/caro2009ecmlpkdd-clustr/}
}