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Cao, F., Estert, M., Qian, W. and Zhou, A. (2006) Density-Based Clustering over an Evolving Data Stream with Noise. Proceedings of the 2006 SIAM International Conference on Data Mining, Bethesda, 20-22 April 2006, 328-339.
https://doi.org/10.1137/1.9781611972764.29

has been cited by the following article:

• TITLE: Selective Updating for Customer Segmentation under Concept Drift

  AUTHORS: Maria El Maaroud, Chi Zhang

  KEYWORDS: Customer Segmentation, Concept Drift, Model Replacement, BIRCH, MiniBatchKMeans

  JOURNAL NAME: Open Access Library Journal, Vol.13 No.11, November 11, 2026

  ABSTRACT: Scheduled refitting can replace an effective customer segmentation even when a newly fitted model offers little improvement. We examine selective replacement for batchwise customer segmentation under concept drift, comparing BIRCH and MiniBatchKMeans under static, periodic, quality-only, and reward-based policies on Instacart, Ta-Feng, and Olist. The reward policy has the highest reported mean deployed silhouette in all six dataset-algorithm combinations; its advantage over quality-only is 0.0045 - 0.0170 silhouette points. Five supplied paired seed effects per combination are positive for reward versus each baseline, with seed-bootstrap intervals excluding zero. These same-snapshot comparisons do not establish temporally disjoint gains or isolate the reward’s stability term. The raw run record is not yet archived; label-aligned stability and denominator-aware event analyses are not reported.