Nonparametric Bootstrap Likelihood Estimation to Investigate the Chance Set-Up on Clustering Results

• Published in: IEEE open journal of the Computer Society Vol. 6; pp. 438 - 448
• Main Authors: Elnour, Ammar, Yang, Wencheng, Li, Yan
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; bootstrap likelihood; Clustering; Clustering algorithms; Datasets; Estimation; Knowledge discovery; Machine learning; Noise measurement; Partitioning algorithms; randomness; Resampling; significance testing; Statistical learning; statistical machine learning; Statistical methods; Surveys; Testing; Uncertainty; validity testing

Clustering algorithms are widely used in the knowledge discovery domain, but concerns and questions about the validity of the results must be considered. The datasets commonly used for clustering tasks are often large and scale-free, making conventional statistical techniques inadequate for analyzin...