Abstract
Heatmaps, also called matrix visualisations, are a popular technique for visualising boolean data. They are easy to understand, and provide a relatively loss-free image of a given dataset. However, they are also highly dependent on the order of rows and columns chosen. We propose a novel technique, called ConvoMap, for ordering the rows and columns of a matrix such that the resulting image represents data faithfully. ConvoMap uses a novel optimisation criterion based on convolution to obtain a good column and row order. While in this paper we focus on the creation of images for exploratory data analysis in binary data, the simplicity of the ConvoMap optimisation criterion could allow for the creation of images for many other types of data as well.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
- 4.
Results on real-world data can be found in [2].
References
Chen, C-h, Härdle, W., Unwin, A.: Handbook of Data Visualization. Springer, Berlin (2008). https://doi.org/10.1007/978-3-540-33037-0
Bollen, T., Leurquin, G.: Faithful visualization of categorical data. Master’s thesis (2017). Université catholique de Louvain, Louvain-la-Neuve
Hubert, L.J.: Some applications of graph theory and related nonmetric techniques to problems of approximate seriation: the case of symmetric proximity measures. Br. J. Math. Stat. Psychol. 27, 133–153 (1974)
Atkins, J.E., Boman, E.G., Hendrickson, B.: A spectral algorithm for seriation and the consecutive ones problem. SIAM J. Comput. 28, 297–310 (1998)
Hahsler, M., Hornik, K. Buchta, C.: Getting things in order: an introduction to the R package seriation. J. Stat. Softw. Artic. 25(3) (2008)
Liiv, I.: Seriation and matrix reordering methods: an historical overview. Stat. Anal. Data Min. 3(2), 70–91 (2010)
Kaski, P., Junttila, E.: Segmented nestedness in binary data. In: Proceedings of the Eleventh SIAM International Conference on Data Mining, pp. 235–246 (2011)
Garriga, G.C., Junttila, E., Mannila, H.: Banded structure in binary matrices. Knowl. Inf. Syst. 28(1), 197–226 (2011)
Junttila, E.: Patterns in permuted binary matrices. PhD thesis, University of Helsinki (2011)
Mäkinen, E., Siirtola, H.: The barycenter heuristic and the reorderable matrix. Inf. (Slov.) 29(3), 357–364 (2005)
Christofides, N.: Worst-case analysis of a new heuristic for the travelling salesman problem, Report 388, Graduate School of Industrial Administration, CMU (1976)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Bollen, T., Leurquin, G., Nijssen, S. (2018). ConvoMap: Using Convolution to Order Boolean Data. In: Duivesteijn, W., Siebes, A., Ukkonen, A. (eds) Advances in Intelligent Data Analysis XVII. IDA 2018. Lecture Notes in Computer Science(), vol 11191. Springer, Cham. https://doi.org/10.1007/978-3-030-01768-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-01768-2_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01767-5
Online ISBN: 978-3-030-01768-2
eBook Packages: Computer ScienceComputer Science (R0)