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.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Ahn, J.-w., Brusilovsky, P., Grady, J., He, D., Syn, S.Y.: Open user profiles for adaptive news systems: help or harm? Int. conf. WWW 2007 (2007)
Olsen, K.A., Korfhage, R.R., Sochats, K.M., Spring, M.B., Williams, J.G.: Visualisation of a document collection: The VIBE system. In: Inf. Proc. and Managem. (1993)
Shneiderman, B.: Designing the User Interface. Addison Wesley, Reading (1997)
Zytkow, J.M., Rauch, J.: Circle Graphs: New Visualization Tools for Text-Mining. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 277–282. Springer, Heidelberg (1999)
Kliger, J.: Methods for Visualizing User Models. MIT Media Lab, Cambridge
Don, A., Zheleva, E., Gregory, M., Tarkan, S., Auvil, L., Clement, T., Shneiderman, B., Plaisant, C.: Discovering interesting usage patterns in text collections: Integrating text mining with visualization. HCIL Technical report 2007-08
Seo, J., Shneiderman, B.: A Rank-by-Feature Framework for Unsupervised Multidimensional Data Exploration Using Low Dimensional Projections. In: Infovis 2004 (2004)
Inselberg, A.: Visual Data Mining with Parallel Coordinates. Comp. Statistics (1998)
Nocke, T., Schumann, H.: Goals of Analysis for Visualization and Visual Data Mining Tasks. In: CODATA Workshop Information, Presentation and Design (2004)
Catarci, T., Costabile, M.F., Levialdi, S., Batini, C.: Visual Query Systems for Databases: A Survey. Journal of Visual Languages and Computing 8
Derthick, M., Kolojejchick, J., Roth, S.F.: An Interactive Visual Query Environment for Exploring Data
Qi, Y., Candan, K.S.: CUTS: CUrvature-Based Development Pattern Analysis and Segmentation for Blogs and other Text Streams. In: Hypertext 2006 (2006)
Hu., M.J., Lew, M.S.: The MIR Flickr Retrieval Evaluation
Berchtold, S., Jagadish, H.V., Ross, K.A.: Independence Diagrams: A Technique for Visual Data Mining. AT&T Laboratories (1998)
Deerwester, S., Dumais, S., Furnas, G., Harshman, R., Landauer, T., Lochbaum, K., Streeter, L.: Computer Information Retrieval using Latent Semantic Structure
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Di Caro, L., Jaimes, A. (2009). ClusTR: Exploring Multivariate Cluster Correlations and Topic Trends. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2009. Lecture Notes in Computer Science(), vol 5782. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04174-7_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-04174-7_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04173-0
Online ISBN: 978-3-642-04174-7
eBook Packages: Computer ScienceComputer Science (R0)