Active clustering data streams with affinity propagation
Sameh Abdulah, Walid Atwa, Ahmed M. Abdelmoniem
Abstract
Most existing applications have a large number of evolving data streams. Clustering data streams is still a critical problem for these applications as the data are evolving and changes over time. Most existing algorithms are unsupervised learning in which background information is useless. This paper proposes an active clustering algorithm for data stream based on the affinity propagation method, referred to as AAPStream. The affinity propagation aims to identify exemplars and create clusters based on these exemplars. Thus, the objective is to get the most informative exemplars to create the streaming model and predict the new arrival data. We conduct a set of experiments on real-world datasets to compare our algorithm with a state-of-the-art algorithm, and the experimental results show the effectiveness of the proposed algorithm.