Subspace clustering and visualization of data streams
Auteurs
I. Louhi, L. Boudjeloud-Assala, and T. Tamisier
Référence
in Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Porto, Portugal, vol. 3, pp. 259-265, 2017
Description
In this paper, we propose a visual subspace clustering approach for data streams, allowing the user to visually track data stream behavior. Instead of detecting elements changes, the approach shows visually the variables impact on the stream evolution, by visualizing the subspace clustering at different levels in real time. First we apply a clustering on the variables set to obtain subspaces, each subspace consists of homogenous variables subset. Then we cluster the elements within each subspace. The visualization helps to show the approach originality and its usefulness in data streams processing.
