Density-Based Clustering Based on Hierarchical Density Estimates

UCI Machine Learning Repository

Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190

Untitled work

(1996) · Cited by 20,179

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

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

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

Projection Path Explorer: Exploring Visual Patterns in Projected Decision-making Paths

Hinterreiter, A., Steinparz, C., Schöfl, M., Stitz, H., & Streit, M. (2021) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3387165 · Cited by 23

dbscan: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms

Hahsler, M., & Piekenbrock, M. (2015) · doi:10.32614/cran.package.dbscan · Cited by 24

Human- and lightning-caused wildland fire ignition clusters in British Columbia, Canada

Coogan, S. C. P., Aftergood, O., & Flannigan, M. (2022) · International Journal of Wildland Fire · doi:10.1071/wf21177 · Cited by 23

FunFun : ITS ‐based functional annotator of fungal communities

Krivonos, D. V., Konanov, D. N., & Ilina, E. N. (2023) · Ecology and Evolution · doi:10.1002/ece3.9874 · Cited by 23

Ecological dynamic regimes: Identification, characterization, and comparison

Sánchez‐Pinillos, M., Kéfi, S., Cáceres, M. D., & Dakos, V. (2023) · Ecological Monographs · doi:10.1002/ecm.1589 · Cited by 23

Integrative temporal multi-omics reveals uncoupling of transcriptome and proteome during human T cell activation

Weerakoon, H. T. W., Mohamed, A., Wong, Y., Chen, J., Senadheera, B., Haigh, O. L., Watkins, T. S., Kazakoff, S. H., Mukhopadhyay, P., Mulvenna, J. P., et al. (2024) · npj Systems Biology and Applications · doi:10.1038/s41540-024-00346-4 · Cited by 24

SHERPA: Explainable Robust Algorithms for Privacy-Preserved Federated Learning in Future Networks to Defend Against Data Poisoning Attacks

Sandeepa, C., Siniarski, B., Wang, S., & Liyanage, M. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00271 · Cited by 23

Semantic navigation for automated robotic inspection and indoor environment quality monitoring

Hu, D., & Gan, V. J. L. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105949 · Cited by 23

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Wu, Z., Lu, H., Paoletti, M. E., Su, H., Jing, W., & Haut, J. M. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3540385 · Cited by 23

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Chong, Y., Peng, C., Zhang, C., Wang, Y., Feng, W., & Pan, S. (2021) · Applied Intelligence · doi:10.1007/s10489-020-02107-2 · Cited by 23

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Bertocco, G. C., Andaló, F., & Rocha, A. D. R. (2021) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2021.3107157 · Cited by 25

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Hassani, M., Spaus, P., & Seidl, T. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-08979-9_11 · Cited by 23

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Soh, D. C. L., Krishnan, S. B., Abraham, J. A., Xian, L. K., Tseng, K. J., & Yongyi, J. F. (2022) · Energies · doi:10.3390/en15020508 · Cited by 23

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Zhang, Z., Chen, X., & Zio, E. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109164 · Cited by 23

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Tadres, D., Wong, P. H., To, T., Moehlis, J., & Louis, M. (2022) · Science Advances · doi:10.1126/sciadv.ade7209 · Cited by 27

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Jeon, H., Quadri, G. J., Lee, H., Rosen, P., Szafir, D. A., & Seo, J. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3327201 · Cited by 23

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Crisan, A., Gardy, J. L., & Munzner, T. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty832 · Cited by 23

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Strezoski, G., van Noord, N., & Worring, M. (2019) · International Conference on Multimedia Retrieval · doi:10.1145/3323873.3325009 · Cited by 23

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Solihah, B., Azhari, A., & Musdholifah, A. (2020) · PeerJ Computer Science · doi:10.7717/peerj-cs.275 · Cited by 23

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Kundishora, A. J., Allington, G., McGee, S., Mekbib, K. Y., Gainullin, V. G., Timberlake, A. T., Nelson-Williams, C. J., Kiziltug, E., Smith, H. L., Ocken, J., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02238-2 · Cited by 22

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Lyu, X., Han, Y., Wang, W., Liu, J., Wang, B., Chen, K., Li, Y., Liu, J., & Zhang, X. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3445637 · Cited by 25

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Hassani, M., Spaus, P., Cuzzocrea, A., & Seidl, T. (2016) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid.2016.102 · Cited by 22

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Li, D., Lin, L.-S., Chen, C.-C., & Yu, W.-H. (2019) · Soft Computing · doi:10.1007/s00500-018-03744-z · Cited by 22

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Lezhnina, O. (2023) · Frontiers in Research Metrics and Analytics · doi:10.3389/frma.2023.1271385 · Cited by 30

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Korakakis, M., Phivos Mylonas, B., & Evaggelos Spyrou, A. (2016) · International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) · doi:10.1109/smap.2016.7753385 · Cited by 22

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He, B., Zhang, Y., Chen, Y., & Gu, Z. (2018) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi7060203 · Cited by 22

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Franklin, M. W., & Slusky, J. S. G. (2018) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.06.013 · Cited by 22

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Latysheva, А. S., Zolottsev, V. А., Veselovsky, A. V., Scherbakov, K. A., Morozevich, G. E., Pokrovsky, V. S., Новиков, Р. А., Timofeev, V. P., Tkachev, Y. V., & Misharin, A. Y. (2019) · Steroids · doi:10.1016/j.steroids.2019.108534 · Cited by 22

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Barylyuk, K. V., Kořený, L., Ke, H., Butterworth, S. W., Crook, O. M., Lassadi, I., Gupta, V., Tromer, E. C., Mourier, T., Stevens, T. J., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.23.057125 · Cited by 22

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Li, C.-H., & Tabor, D. P. (2023) · Chemical Science · doi:10.1039/d3sc03781a · Cited by 23

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Lee, J., & Jang, K. (2024) · Accident Analysis & Prevention · doi:10.1016/j.aap.2024.107779 · Cited by 21

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Tokuda, E. K., Comin, C. H., & Costa, L. D. F. (2021) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2021.126433 · Cited by 21

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Niu, X., Zhu, J., Wu, C. Q., & Wang, S. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103932 · Cited by 21

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Mirzaei, O., Vasilenko, R., Kirda, E., Lu, L. J., & Kharraz, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-80825-9_7 · Cited by 20

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Jiang, Z., Jiang, X., Hazimeh, A., Tang, C., Zhang, C., & Payer, M. (2021) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3460120.3485364 · Cited by 22

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Onyango, S. O., Owiredu, S. A., Kim, K.-I., & Yoo, S.-L. (2022) · Sensors · doi:10.3390/s22228639 · Cited by 21

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Zhang, Q., Yang, Y., Li, P., Andersson, O., & Jensfelt, P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73232-4_20 · Cited by 21

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