High-Dimensional Wide Gap $k$-Means Versus Clustering Axioms
arXiv:2211.17036v1h-index: 12
Originality Synthesis-oriented
AI Analysis
This work tackles a foundational theoretical problem in clustering for researchers, but appears incremental as it builds on prior efforts to resolve the axiom contradictions.
The paper addresses the contradiction in Kleinberg's clustering axioms by proposing an approach that embeds data in high-dimensional space with wide gaps between clusters, though no specific results or numbers are provided.
Kleinberg's axioms for distance based clustering proved to be contradictory. Various efforts have been made to overcome this problem. Here we make an attempt to handle the issue by embedding in high-dimensional space and granting wide gaps between clusters.