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.
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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
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DOI: https://doi.org/10.1007/978-3-319-70139-4_8
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