Abstract
Visualization methods are important to describe the underlying structure of a data set. When the data is not described as a vector of numerical values, a visualization can be obtained through the reordering of the corresponding similarity matrix. Although several methods of reordering exist, they all need the complete similarity matrix in memory. However, this is not possible for the analysis of dynamic data sets. The goal of this paper is to propose an original algorithm for the incremental reordering of a similarity matrix adapted to dynamic data sets. The proposed method is compared with state-of-the-art algorithms for static data-sets and applied to a dynamic data-set in order to demonstrate its efficiency.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bar-Joseph, Z., Gifford, D.K., Jaakkola, T.S.: Fast optimal leaf ordering for hierarchical clustering. Bioinformatics 17(Suppl. 1), S22–S29 (2001)
Barnard, S.T., Pothen, A., Simon, H.D.: A spectral algorithm for envelope reduction of sparse matrices. In: Proceedings of the 1993 ACM/IEEE Conference on Supercomputing 1993, pp. 493–502. ACM, New York (1993)
Behrisch, M., Bach, B., Riche, N.H., Schreck, T., Fekete, J.D.: Matrix reordering methods for table and network visualization. In: Computer Graphics Forum (2016)
Bezdek, J.C., Hathaway, R.J.: VAT: a tool for visual assessment of (cluster) tendency. In: International Joint Conference on Neural Networks (IJCNN), vol. 3, pp. 2225–2230 (2002)
Buja, A., Swayne, D.F., Littman, M.L., Dean, N., Hofmann, H., Chen, L.: Data visualization with multidimensional scaling. J. Comput. Graph. Stat. 17(2), 444–472 (2008)
Caraux, G., Pinloche, S.: Permutmatrix: a graphical environment to arrange gene expression profiles in optimal linear order. Bioinformatics 21(7), 1280–1281 (2005)
Chen, C.H., Hrdle, W., Unwin, A.: Handbook of Data Visualization, 1st edn. Springer-Verlag TELOS, Santa Clara (2008). doi:10.1007/978-3-540-33037-0
Conover, W.J.: Practical Nonparametric Statistics. Wiley, New York (1981)
Ding, C., He, X.: Linearized cluster assignment via spectral ordering. In: International Conference on Machine Learning, New York, NY, USA, p. 30 (2004)
Gama, J.: Knowledge Discovery from Data Streams, 1st edn. Chapman & Hall/CRC, Boca Raton (2010)
Gruvaeus, G., Wainer, H.: Two additions to hierarchical cluster analysis. Br. J. Math. Stat. Psychol. 25(2), 200–206 (1972)
Hahsler, M., Hornik, K., Buchta, C.: Getting things in order: an introduction to the R package seriation. J. Stat. Softw. 25(3), 1–34 (2008)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn., USA (2006)
Liiv, I.: Seriation and matrix reordering methods: an historical overview. Stat. Anal. Data Mining 3(2), 70–91 (2010)
Mount, D.W.: Sequence and genome analysis. Bioinformatics: Cold Spring Harbour Laboratory Press: Cold Spring Harbour 2 (2004)
Rastin, P., Matei, B., Cabanes, G., El Baghdadi, I.: Signal-based autonomous clustering for relational data. In: International Joint Conference on Neural Networks, IJCNN 2017 (2017)
Rosenkrantz, D.J., Stearns, R.E., Lewis II, P.M.: An analysis of several heuristics for the traveling salesman problem. SIAM J. Comput. 6(3), 563–581 (1977)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Rastin, P., Matei, B. (2017). Incremental Matrix Reordering for Similarity-Based Dynamic Data Sets. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-70139-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70138-7
Online ISBN: 978-3-319-70139-4
eBook Packages: Computer ScienceComputer Science (R0)