Abstract
Information visualization is gaining importance in data mining and transactional data has long been an important target for data miners. We propose a novel approach for visualizing transactional data using multiple clustering results for knowledge discovery. This scheme necessitates us to relate different clustering results in a comprehensive manner. Thus we have invented a method for attributing colors to clusters of different clustering results based on minimal transversals. The effectiveness of our method VisuMClust has been confirmed with experiments using artificial and real-world data sets.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Afrati, F.N., Gionis, A., Mannila, H.: Approximating a Collection of Frequent Sets. In: Proc. 10th Int. Conf. on Knowledge Discovery and Data Mining (KDD 2004), Seattle, WA, August 2004, pp. 12–19 (2004)
Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Database. ACM SIGMOD 22(2), 207–216 (1993)
Berge, C.: Hypergraph. North Holland, Amsterdam (1989)
Berkhin, P.: Survey of Clustering Data Mining Techniques. Technical report, Accrue Software, San Jose, CA (2002)
Cadez, I.V., Heckerman, D., Meek, C., Smyth, P., White, S.: Model-Based Clustering and Visualization of Navigation Patterns on a Web Site. Data Mining and Knowledge Discovery 7(4), 399–424 (2003)
Cadez, I.V., Smyth, P., Mannila, H.: Probabilistic Modeling of Transaction Data with Applications to Profiling, Visualization, and Prediction. In: Proc. 7th Int. Conf. on Knowledge Discovery and Data Mining (KDD 2001), San Francisco, California, USA, August 2001, pp. 37–46 (2001)
Card, S.K., Makinlay, J.D., Shneiderman, B. (eds.): Readings in Information Visualization. Morgan Kaufmann, San Francisco (1999)
Durand, N., Crémilleux, B.: ECCLAT: a New Approach of Clusters Discovery in Categorical Data. In: Proc. 22nd SGAI Int. Conf. on Knowledge Based Systems and Applied Artificial Intelligence (ES 2002), Cambridge, UK, pp. 177–190 (2002)
Eiter, T., Gottlob, G.: Identifying the Minimal Transversals of a Hypergraph and Related Problems. SIAM Journal on Computing Archive 24(6), 1278–1304 (1995)
Estivill-Castro, V.: Why So Many Clustering Algorithms - A Position Paper. ACM SIGKDD Explorations 4(1), 65–75 (2002)
Fayyad, U., Grinstein, G.G., Wierse, A.: Information Visualization in Data Mining and Knowledge Discovery. Morgan Kaufmann, San Francisco (2002)
Gionis, A., Mannila, H., Tsaparas, P.: Clustering Aggregation. In: Proc. 21st Int. Conf. on Data Engineering (ICDE 2005), Tokyo, Japan, April 2005, pp. 341–352 (2005)
Gunopulos, D., Khardon, R., Mannila, H., Toivonen, H.: Data Mining, Hypergraph Transversals, and Machine Learning. In: Proc. 16th Symposium on Principles of Database Systems (PODS 1997), Tucson, Arizona, May 1997, pp. 209–216 (1997)
Hébert, C.: Enumerating the Minimal Transversals of a Hypergraph Using Galois Connections. Technical report, Univ. Caen Basse-Normandie, France (2005)
Hipp, J., Güntzer, H., Nakhaeizadeh, G.: Algorithms for Association Rule Mining - A General Survey and Comparison. SIGKDD Explorations 2(1), 58–64 (2000)
Squire, D.M., Squire, D.M.: Visualization of Cluster Changes by Comparing Self-organizing Maps. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 410–419. Springer, Heidelberg (2005)
Pensa, R., Robardet, C., Boulicaut, J.-F.: A Bi-clustering Framework for Categorical Data. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 643–650. Springer, Heidelberg (2005)
Suzuki, E., Watanabe, T., Yokoi, H., Takabayashi, K.: Detecting Interesting Exceptions from Medical Test Data with Visual Summarization. In: Proc. 3rd IEEE International Conf. on Data Mining (ICDM 2003), pp. 315–322 (2003)
Tufte, E.: The Visual Display of Quantitative Information. Graphics Press (2001)
Westphal, C., Blaxton, T.: Data Mining Solutions. John Wiley and Sons, New York (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Durand, N., Crémilleux, B., Suzuki, E. (2006). Visualizing Transactional Data with Multiple Clusterings for Knowledge Discovery. In: Esposito, F., Raś, Z.W., Malerba, D., Semeraro, G. (eds) Foundations of Intelligent Systems. ISMIS 2006. Lecture Notes in Computer Science(), vol 4203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875604_7
Download citation
DOI: https://doi.org/10.1007/11875604_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45764-0
Online ISBN: 978-3-540-45766-4
eBook Packages: Computer ScienceComputer Science (R0)