Abstract
We propose in this paper a new approach to detect and visualize the change in a streaming clustering. This approach can be used to explore visually the data streams. We assume that the data stream structure can be different during the time. Our objective is to alert the user on the structure change during the time period. A common approach to deal with data streams is to observe and process it in a window. The principle of the proposed approach is to apply a data exploration method on each window. We then propose to visualize the change between all windows for each extracted cluster. The user can investigate more precisely the change between the two windows through a visual projection for each extracted cluster.
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Boudjeloud-Assala, L., Pinheiro, P., Blansché, A., Tamisier, T., Otjaques, B. (2015). Visual and Dynamic Change Detection for Data Streams. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9491. Springer, Cham. https://doi.org/10.1007/978-3-319-26555-1_45
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