  We present S+t-SNE, an adaptation of the t-SNE algorithm designed to handle
infinite data streams. The core idea behind S+t-SNE is to update the t-SNE
embedding incrementally as new data arrives, ensuring scalability and
adaptability to handle streaming scenarios. By selecting the most important
points at each step, the algorithm ensures scalability while keeping
informative visualisations. Employing a blind method for drift management
adjusts the embedding space, facilitating continuous visualisation of evolving
data dynamics. Our experimental evaluations demonstrate the effectiveness and
efficiency of S+t-SNE. The results highlight its ability to capture patterns in
a streaming scenario. We hope our approach offers researchers and practitioners
a real-time tool for understanding and interpreting high-dimensional data.
