Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Characterization of Linkage-Based Hierarchical Clustering
Authors: Margareta Ackerman, Shai Ben-David
JMLR 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We identify two properties of hierarchical algorithms, and prove that linkagebased algorithms are the only ones that satisfy both of these properties. Our characterization clearly delineates the di๏ฌerence between linkage-based algorithms and other hierarchical methods. We formulate an intuitive notion of locality of a hierarchical algorithm that distinguishes between linkage-based and global hierarchical algorithms like bisecting k-means, and prove that popular divisive hierarchical algorithms produce clusterings that cannot be produced by any linkage-based algorithm. |
| Researcher Affiliation | Academia | Margareta Ackerman EMAIL Department of Computer Science San Jose State University San Jose, CA Shai Ben-David EMAIL D.R.C. School of Computer Science University of Waterloo Waterloo, ON |
| Pseudocode | No | The paper describes algorithms conceptually through definitions and properties but does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is a theoretical work focusing on characterization and proofs, and does not mention the release of any source code or provide links to repositories. |
| Open Datasets | No | The paper defines 'data sets' as theoretical constructs (X, d) for its mathematical framework, but it does not utilize or refer to any specific publicly available datasets for experimental evaluation. |
| Dataset Splits | No | The paper is theoretical and does not perform experiments on datasets, therefore it does not discuss training/test/validation dataset splits. |
| Hardware Specification | No | The paper presents a theoretical characterization of hierarchical clustering algorithms and does not involve any experimental evaluations, hence no hardware specifications are provided. |
| Software Dependencies | No | The paper is entirely theoretical and does not describe any implementation or experimental setup that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper focuses on theoretical characterization and proofs, and does not include any experimental evaluations or specific experimental setup details such as hyperparameters or training configurations. |