Abstract
Data mining techniques allow users to discover novelty in huge amounts of data. Frequent pattern methods have proved to be efficient, but the extracted patterns are often too numerous and thus difficult to analyse by end-users. In this paper, we focus on sequential pattern mining and propose a new visualization system, which aims at helping end-users to analyse extracted knowledge and to highlight the novelty according to referenced biological document databases. Our system is based on two visualization techniques: Clouds and solar systems. We show that these techniques are very helpful for identifying associations and hierarchical relationships between patterns among related documents. Sequential patterns extracted from gene data using our system were successfully evaluated by two biology laboratories working on Alzheimers disease and cancer.
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References
Cong, G.A., Tung, X., Pan, F., Yang, J.: Farmer: Finding interesting rule groups in microarray datasets. In: SIGMOD Conference, pp. 143–154 (2004)
Salle, P., Bringay, S., Teisseire, M., Chakkour, F., Roche, M., Rassoul, R.A., Verdier, J.M., Devau, G.: Genemining: Identification, visualization, and interpretation of brain ageing signatures. In: MIE, pp. 767–771 (2009)
Zeeberg, B.R., Feng, W., Wang, G., Wang, M.D., Fojo, A.T., Sunshine, M., Narasimhan, S., Kane, D.W., Reinhold, W.C., Lababidi, S., Bussey, K.J., Riss, J., Barrett, J.C., Weinstein, J.N.: Gominer: a resource for biological interpretation of genomic and proteomic data. Genome Biol. 4, 28 (2003)
Salle, P., Bringay, S., Teisseire, M.: Mining discriminant sequential patterns for aging brain. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds.) Artificial Intelligence in Medicine. LNCS, vol. 5651, pp. 365–369. Springer, Heidelberg (2009)
Saneifar, H., Bringay, S., Laurent, A., Teisseire, M.: S2mp: Similarity measure for sequential patterns. In: AusDM, pp. 95–104 (2008)
Shneiderman, B.: The eyes have it: A task by data type taxonomy for information visualizations. In: VL, pp. 336–343 (1996)
Chi, E.H.-h, Riedl, J., Shoop, E., Carlis, J.V., Retzel, E., Barry, P.: Flexible information visualization of multivariate data from biological sequence similarity searches. In: IEEE Visualization, pp. 133–140 (1996)
Lungu, M., Xu, K.: Biomedical information visualization. In: Kerren, A., Ebert, A., Meyer, J. (eds.) GI-Dagstuhl Research Seminar 2007. LNCS, vol. 4417, pp. 311–342. Springer, Heidelberg (2007)
Brog, I., Groenen, P.: Modern multidimensional scaling: Theory and applications. Springer, New York (1997)
Gansner, E.R., Koren, Y., North, S.: Graph drawing by stress majorization. In: GDRAWING: Conference on Graph Drawing (GD) (2004)
de Leeuw, J.: Convergence of the majorization method for multidimensional scaling. J. Classification 5, 163–180 (1988)
Priyantha, N.B., Balakrishnan, H., Demaine, E.D., Teller, S.J.: Anchor-free distributed localization in sensor networks. In: Akyildiz, I.F., Estrin, D., Culler, D.E., Srivastava, M.B. (eds.) SenSys, pp. 340–341. ACM, New York (2003)
Gansner, E.R., Hu, Y.: Efficient node overlap removal using a proximity stress model. In: Tollis, I.G., Patrignani, M. (eds.) GD 2008. LNCS, vol. 5417, pp. 206–217. Springer, Heidelberg (2009)
Nguyen, T., Zhang, J.: A novel visualization model for web search results. IEEE Trans. Vis. Comput. Graph 12, 981–988 (2006)
Jacquemin, C., Folch, H., Garcia, K., Nugier, S.: Visualisation interactive d’espaces documentaires. Information Interaction Intelligence 5, 59–84 (2005)
Ammenwerth, E.: Can evaluation studies benefit from triangulation? a case study. International Journal of Medical Informatics 70, 237–248 (2003)
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Sallaberry, A., Pecheur, N., Bringay, S., Roche, M., Teisseire, M. (2010). Discovering Novelty in Gene Data: From Sequential Patterns to Visualization. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2010. Lecture Notes in Computer Science, vol 6455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17277-9_55
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DOI: https://doi.org/10.1007/978-3-642-17277-9_55
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