Cited by 2,680Advanced Clustering Algorithms ResearchData Management and AlgorithmsBayesian Methods and Mixture Models

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Density-Based Clustering Based on Hierarchical Density Estimates

Campello, Ricardo J. G. B., Moulavi, Davoud, Sander, Joerg·2013·Lecture Notes in Computer Science

DOI 10.1007/978-3-642-37456-2_14

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Untitled work
(1996) · Cited by 20,179
Self-citation
Comparing partitions
Hubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219
Introduction to Data Mining
Chiu, S., & Tavella, D. (2008) · Routledge eBooks · doi:10.4324/9780080878096-12 · Cited by 6,987
Untitled work
(1975) · CERN Document Server (European Organization for Nuclear Research) · Cited by 5,958
Untitled work
Ankerst, M. (1999) · doi:10.1145/304181.304187 · Cited by 3,982
Self-citation
The Amsterdam Library of Object Images
Geusebroek, J., Burghouts, G. J., & Smeulders, A. W. M. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000042993.50813.60 · Cited by 919
Density‐based clustering
Kriegel, H., Kröger, P., Sander, J., & Zimek, A. (2011) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.30 · Cited by 900
Self-citation
Fast and effective text mining using linear-time document clustering
Larsen, B., & Aone, C. (1999) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312186 · Cited by 869
Model-based clustering and data transformations for gene expression data
Yeung, K. Y., Fraley, C., Murua, A., Raftery, A. E., & Ruzzo, W. L. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.10.977 · Cited by 869

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Authors: Campello, R. J. G. B., Moulavi, D., & Sander, J.
Affiliations: University of Alberta (CA)
ORCID: Campello, Ricardo J. G. B.
Venue: Lecture Notes in Computer Science
Publisher: Springer Berlin Heidelberg
Published: 2013
Volume: 7819
Pages: 160-172
ISSN: 0302-9743 (ISSN-L)
1611-3349
DOI: 10.1007/978-3-642-37456-2_14
OpenAlex: W151377110
Cited by: 2,680
References: 27
Citations by year: 2026: 413 · 2025: 497 · 2024: 404 · 2023: 337 · 2022: 271 · 2021: 253 · 2020: 181 · 2019: 148 · 2018: 84 · 2017: 50 · 2016: 24 · 2015: 9 · 2014: 9
FWCI: 9.26
Type: conference paper
Language: en
Open access: closed
Locations: Lecture notes in computer science (publishedVersion)
ResearchOnline - JCU (James Cook University) (submittedVersion)
Lecture Notes in Artificial Intelligence
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
Keywords: density-based clustering, hierarchical clustering, density limit

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Density-Based Clustering Based on Hierarchical Density Estimates

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