RT Journal Article
T1 CRB-NCE: An adaptable cohesion rule-based approach to number of clusters estimation
A1 Rodríguez González, Juan Tinguaro
A1 Gonzalez-Garcia, Xabier
A1 Gómez González, Daniel
A1 Bustince, Humberto
AB Accurate number-of-clusters estimation (NCE) is a central task in many clustering applications, particularly for prototype-based 𝑘-centers methods like 𝑘-Means, which require the number of clusters 𝑘 to be specified in advance. This paper presents CRB-NCE, a general cluster cohesion rule-based framework for NCE integrating three main innovations: (i) the introduction of tail ratios to reliably identify decelerations in sequences of cohesion measures, (ii) a threshold-based rule system supporting accurate NCE, and (iii) an optimization-driven approach to learn these thresholds from synthetic datasets with controlled clustering complexity. Two cohesion measures are considered: inertia (SSE) and a new, scale-invariant metric called the mean coverage index. CRB-NCE is mainly applied to derive general-purpose NCE methods, but, most importantly, it also provides an adaptable framework that enables producing specialized procedures with enhanced performance under specific conditions, such as particular clustering algorithms or overlapping cluster structures. Extensive evaluations on synthetic Gaussian datasets (both standard and high-dimensional), clustering benchmarks, and real-world datasets show that CRB-NCE methods consistently achieve robust and competitive NCE performance with efficient runtimes compared to a broad baseline of internal clustering validity indices and other NCE methods.
PB Elsevier
SN 0031-3203
YR 2026
FD 2026
LK https://hdl.handle.net/20.500.14352/132706
UL https://hdl.handle.net/20.500.14352/132706
LA eng
NO 2025 Acuerdos transformativos CRUE
NO Ministerio de Ciencia e Innovación
DS Docta Complutense
RD 21 abr 2026