SNCStream+: Extending A High Quality True Anytime Data Stream Clustering Algorithm
Résumé
Data Stream Clustering is an active area of research which requires efficient algorithms
capable of finding and updating clusters incrementally as data arrives.
On top of that, due to the inherent evolving nature of data streams, it is expected
that algorithms undergo both concept drifts and evolutions, which must
be taken into account by the clustering algorithm, allowing incremental clustering
updates. In this paper we present the Social Network Clusterer Stream+
(SNCStream+). SNCStream+ tackles the data stream clustering problem as a
network formation and evolution problem, where instances and micro-clusters
form clusters based on homophily. Our proposal has its parameters analyzed
and it is evaluated in a broad set of problems against literature baselines. Results
show that SNCStream+ achieves superior clustering quality (CMM), and
feasible processing time and memory space usage.