Abstract
In this paper, we introduce the clustered parallel coordinates based on the progressively processing technique to solve the problems of clutter and memory limitation when visualizing and exploring large-scale data. The clustering method is based on the k-means method so that it is possible to partition large-scale datasets with relatively high speed. The progressively processing technique called out-of-core feature can enable the system processes input data part by part, which reduces the requirement of memory capacity.
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Zhang, C., Sakamoto, N., Koyamada, K. (2012). Clustered Parallel Coordinates with High-Speed k-Means Algorithm and Out-of-Core Feature. In: Kim, JH., Lee, K., Tanaka, S., Park, SH. (eds) Advanced Methods, Techniques, and Applications in Modeling and Simulation. Proceedings in Information and Communications Technology, vol 4. Springer, Tokyo. https://doi.org/10.1007/978-4-431-54216-2_48
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DOI: https://doi.org/10.1007/978-4-431-54216-2_48
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-54215-5
Online ISBN: 978-4-431-54216-2
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