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Examining most streaming clustering algorithms leads to the understandingthat they are actually incremental classification models. They model existingand newly discovered structures via summary information that we callfootprints. Incoming data is normally assigned a crisp label (into one of thestructures) and that structure's footprint is incrementally updated. There isno reason that these assignments need to be crisp. In this paper, we propose anew streaming classification algorithm that uses Neural Gas prototypes asfootprints and produces a possibilistic label vector (of typicalities) for eachincoming vector. These typicalities are generated by a modified possibilistick-nearest neighbor algorithm. The approach is tested on synthetic and realimage datasets. We compare our approach to three other streaming classifiersbased on the Adaptive Random Forest, Very Fast Decision Rules, and theDenStream algorithm with excellent results.

StreamSoNG: A Soft Streaming Classification Approach