  Online incremental clustering of sequentially incoming data without prior
knowledge suffers from changing cluster numbers and tends to fall into local
extrema according to given data order. To overcome these limitations, we
propose a stabilized developmental resonance network (s-DRN). First, we analyze
the instability of the conventional choice function during the node activation
process and design a scalable activation function to make clustering
performance stable over all input data scales. Next, we devise three criteria
for the node grouping algorithm: distance, intersection over union (IoU) and
size criteria. The proposed node grouping algorithm effectively excludes
unnecessary clusters from incrementally created clusters, diminishes the
performance dependency on vigilance parameters and makes the clustering process
robust. To verify the performance of the proposed s-DRN model, comparative
studies are conducted on six real-world datasets whose statistical
characteristics are distinctive. The comparative studies demonstrate the
proposed s-DRN outperforms baselines in terms of stability and accuracy.
