Under the reported settings, K-Means++ achieved the best internal clustering indices, though the K-Means baseline used a single initialization¶
Claim · All claims
Evidence · 1 study · associationalr1·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Among five benchmarked algorithms, K-Means++ achieved the highest Silhouette (0.62), lowest Davies-Bouldin (0.47), and highest Calinski-Harabasz (78.62) values under the reported evaluation settings. → Najem 2026
Evidence¶
Najem 2026¶
Najem, K., Zaoui Seghroucheni, Y., & Ziti, S. (2026). Behavioral clustering for adaptive learning object recommendation in higher education using learning analytics for personalized student success. Discover Education. https://doi.org/10.1007/s44217-026-02181-7
q2 · i? · associational · r1
Comparative benchmarking of five clustering algorithms (K-Means, K-Means++, DBSCAN, GMM, Hierarchical) on internal indices; K-Means++ led on Silhouette, Davies-Bouldin, and Calinski-Harabasz, followed by DBSCAN, GMM, and Hierarchical.
"the highest Silhouette (0.62), the lowest Davies-Bouldin (0.47), and the highest Calinski-Harabasz (78.62) value were achieved by K-Means++ among others, indicating that it performed best."
Discussion¶
Related Claims¶
- K-Means++ clustering of self-reported behavioral proxies yields six distinct, stable learner profiles (k = 6, Silhouette = 0.62) — possibly the same claim (merge candidate)
- LCF outperformed k-means, RPMM, and SS-RPMM on cluster validity indices in the same PSY 101 data — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
kmeans-plus-best-benchmark |
title |
Under the reported settings, K-Means++ achieved the best internal clustering indices, though the K-Means baseline used a single initialization |
description |
Under the reported settings, K-Means++ achieved the best internal clustering indices, though the K-Means baseline used a single initialization |
status |
review |
evidence_strength |
mixed |
newest_evidence |
2026 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — najem-2026 |
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