An online clustering algorithm
Kan Li, Fenglan Yao, Ruipeng Liu · 2011
This paper presents a new online clustering algorithm called SAFN which is used to learn continuously evolving clusters from non-stationary data. The SAFN uses a fast adaptive learning procedure to take into account variations over time. In non-stationary and multi-class environment, the SAFN learning procedure consists of five main stages: creation, adaptation, mergence, split and elimination. Experiments are carried out in three kinds of datasets to illustrate the performance of the SAFN algorithm for online clustering. Compared with SAKM algorithm, SAFN algorithm shows better performance in accuracy of clustering and multi-class high-dimension data.