Papers › s-DRN: Stabilized Developmental Resonance Network
s-DRN: Stabilized Developmental Resonance Network
In-Ug Yoon, Ue-Hwan Kim, Jong-Hwan
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections