ClusTR: Exploring Multivariate Cluster Correlations and Topic Trends

Authors:
Luigi Caro;Alejandro Jaimes
Affiliations:
Universita' di Torino, Torino, Italy;Telefonica Research, Madrid, Spain
Venue:
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Year:
2009

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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.