A framework for benchmarking clustering algorithms-Reference-Cited by-同舟云学术

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A framework for benchmarking clustering algorithms

Published:2022-12 Issue: Volume:20 Page:101270
ISSN:2352-7110
Container-title:SoftwareX
language:en
Short-container-title:SoftwareX

Author:

Gagolewski Marek^ORCID

Funder

Australian Research Council

Publisher

Elsevier BV

Subject

Computer Science Applications,Software

Reference36 articles.

1. What are the true clusters?;Hennig;Pattern Recognit Lett,2015

2. Clustering: Science or art?;von Luxburg,2012

3. Benchmarking in cluster analysis: A white paper;Van Mechelen,2018

4. Weighted clustering: Towards solving the user’s dilemma;Ackerman;Pattern Recognit,2021

5. Clustering validation measures;Xiong,2014

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1. Clustering with Minimum Spanning Trees: How Good Can It Be?;Journal of Classification;2024-07-06

2. Normalised Clustering Accuracy: An Asymmetric External Cluster Validity Measure;Journal of Classification;2024-06-28

3. MMM and MMMSynth: Clustering of heterogeneous tabular data, and synthetic data generation;PLOS ONE;2024-04-17

4. Explainable AI for Mixed Data Clustering;Communications in Computer and Information Science;2024

5. AutoClues: Exploring Clustering Pipelines via AutoML and Diversification;Lecture Notes in Computer Science;2024

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