Incremental nearest neighborhood graph for data stream clustering

Auteurs

I. Louhi, L. Boudjeloud-Assala, and T. Tamisier

Référence

in 2016 International Joint Conference on Neural Networks (IJCNN), 24-29 July 2016, doi:10.1109/IJCNN.2016.7727506, 2016

Description

In this paper we deal with one of the most relevant problems in the field of data mining, the real time processing and visualization of data streams. To deal with data streams we propose a novel approach that uses a neighborhood-based clustering. Instead of processing each new element one by one, we propose to process each group of new elements simultaneously. A clustering is applied on each new group using neighborhood graphs. The obtained clusters are then used to incrementally construct a representative graph of the data stream. The data stream graph is visualized in real time with specific visualizations that reflect the processing algorithm. To validate the approach, we apply it to different data streams and we compare it with known data stream clustering approaches.

Lien

doi:10.1109/IJCNN.2016.7727506