High-Dimensional Wide Gap k-Means Versus Clustering Axioms
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 30 Nov 2022.
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7 of 23 references.
References · 7
- 2013Axioms for graph clustering quality functionscited by 6
- 2018Possibility results for graph clustering: A novel consistency axiomcited by 6
- 2022Towards Continuous Consistency Axiomcited by 4
- 2022A Clustering Preserving Transformation for k-Means Algorithm Outputcited by 4
- 2017An Aposteriorical Clusterability Criterion for k-Means++ and Simplicity of Clusteringcited by 3
- 2015Computational Feasibility of Clustering under Clusterability Assumptionscited by 2
- 2016An Effective and Efficient Approach for Clusterability Evaluationcited by 2
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