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Density-Based Clustering Based on Hierarchical Density Estimates
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“Small values produce numerous small clusters potentially appearing only at extreme density thresholds, whereas larger values merge smaller groups into fewer, more stable clusters, that can potentially hide the variability of the patterns observed.”
From Ecological legacies of pre-columbian settlements evident in palm clusters of neotropical mountain forests (2026) · cites this paper as [65]
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“HDBSCAN is adept at clustering outputs from LLMs due to its ability to manage clusters with varied densities, autonomously identify the number of clusters, and filter out noise as outlier data.”
From Large language model trained on clinical oncology data predicts cancer progression (2025) · cites this paper as [54]
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Cites
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190
(1996) · Cited by 20,179
Self-citationHubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219
Chiu, S., & Tavella, D. (2008) · Routledge eBooks · doi:10.4324/9780080878096-12 · Cited by 6,987
(1975) · CERN Document Server (European Organization for Nuclear Research) · Cited by 5,958
Ankerst, M. (1999) · doi:10.1145/304181.304187 · Cited by 3,982
Self-citationThe 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
Kriegel, H., Kröger, P., Sander, J., & Zimek, A. (2011) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.30 · Cited by 900
Self-citationFast 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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Schubert, E., Sander, J., Ester, M., Kriegel, H., & Xu, X. (2017) · ACM Transactions on Database Systems · doi:10.1145/3068335 · Cited by 2,789
Self-citationMachine Learning for Internet of Things Data Analysis: A Survey
Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P. M., & Sheth, A. (2017) · Surrey Research Insight Open Access (The University of Surrey) · Cited by 1,008
Machine learning for internet of things data analysis: a survey
Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018) · Digital Communications and Networks · doi:10.1016/j.dcan.2017.10.002 · Cited by 921
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Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2015) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2733381 · Cited by 915
Self-citationDark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites
Mathur, A., Acar, G., Friedman, M. J., Lucherini, E., Mayer, J., Chetty, M., & Narayanan, A. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359183 · Cited by 816
dbscan: Fast Density-Based Clustering with R
Hahsler, M., Piekenbrock, M., & Doran, D. (2019) · Journal of Statistical Software · doi:10.18637/jss.v091.i01 · Cited by 924
Obesity Shapes Metabolism in the Tumor Microenvironment to Suppress Anti-Tumor Immunity
Ringel, A. E., Drijvers, J. M., Baker, G. J., Catozzi, A., García-Cañaveras, J. C., Gassaway, B. M., Miller, B. C., Juneja, V. R., Nguyen, T. H., Joshi, S., et al. (2020) · Cell · doi:10.1016/j.cell.2020.11.009 · Cited by 780
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McInnes, L., & Healy, J. (2017) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2017.12 · Cited by 630
Study on density peaks clustering based on k-nearest neighbors and principal component analysis
Du, M., Ding, S., & Jia, H. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.02.001 · Cited by 524
Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., & Elger, G. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107864 · Cited by 581
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Altae-Tran, H., Kannan, S., Demircioglu, F. E., Oshiro, R., Nety, S. P., McKay, L. J., Dlakić, M., Inskeep, W. P., Makarova, K. S., Macrae, R. K., et al. (2021) · Science · doi:10.1126/science.abj6856 · Cited by 407
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Glielmo, A., Husic, B. E., Rodríguez, Á., Clementi, C., Noé, F., & Laio, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01195 · Cited by 436
Wagner, M., Yoshihara, M., Douagi, I., Damdimopoulos, A. E., Panula, S., Sophie Petropoulos, A., Lu, H., Pettersson, K., Palm, K., Katayama, S., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14936-3 · Cited by 383
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Ran, X., Xi, Y., Lu, Y., Wang, X., & Lu, Z. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10366-3 · Cited by 433
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Shapson-Coe, A., Januszewski, M., Berger, D. R., Pope, A., Wu, Y., Blakely, T., Schalek, R. L., Li, P. H., Wang, S., Maitin-Shepard, J., et al. (2024) · Science · doi:10.1126/science.adk4858 · Cited by 361
Hoffman, D. P., Shtengel, G. E., Xu, C. S., Campbell, K. R., Freeman, M., Wang, L., Milkie, D. E., Pasolli, H. A., Iyer, N., Bogovic, J., et al. (2020) · Science · doi:10.1126/science.aaz5357 · Cited by 354
Uniform manifold approximation and projection
Healy, J., & McInnes, L. (2024) · Nature Reviews Methods Primers · doi:10.1038/s43586-024-00363-x · Cited by 412
Barylyuk, K., Kořený, L., Ke, H., Butterworth, S., Crook, O. M., Lassadi, I., Gupta, V., Tromer, E. C., Mourier, T., Stevens, T. J., et al. (2020) · Cell Host & Microbe · doi:10.1016/j.chom.2020.09.011 · Cited by 326
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Sainburg, T., Thielk, M., & Gentner, T. Q. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008228 · Cited by 342
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Mrabet, H., Belguith, S., Alhomoud, A., & Jemai, A. (2020) · Sensors · doi:10.3390/s20133625 · Cited by 319
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Angelov, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.09470 · Cited by 706
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Hsu, A. I., & Yttri, E. A. (2021) · Nature Communications · doi:10.1038/s41467-021-25420-x · Cited by 322
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Moulavi, D., Jaskowiak, P. A., Campello, R. J. G. B., Zimek, A., & Sander, J. (2014) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611973440.96 · Cited by 297
Self-citationSelf-Training With Progressive Augmentation for Unsupervised Cross-Domain Person Re-Identification
Zhang, X., Cao, J., Shen, C., & You, M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00831 · Cited by 280
Allaoui, M., Kherfi, M. L., & Cheriet, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-51935-3_34 · Cited by 278
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Bryant, A. C., & Cios, K. J. (2017) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2017.2787640 · Cited by 279
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Bhattacharjee, P., & Mitra, P. (2020) · Frontiers of Computer Science · doi:10.1007/s11704-019-9059-3 · Cited by 273
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Shapson-Coe, A., Januszewski, M., Berger, D. R., Pope, A., Wu, Y., Blakely, T., Schalek, R. L., Li, P. H., Wang, S., Maitin-Shepard, J., et al. (2021) · doi:10.1101/2021.05.29.446289 · Cited by 243
Nacev, B. A., Sanchez-Vega, F., Smith, S. A., Antonescu, C. R., Rosenbaum, E., Shi, H., Tang, C., Socci, N. D., Rana, S., Gularte-Mérida, R., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-30453-x · Cited by 232
Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for Person Re-identification
Zhao, F., Liao, S., Xie, G.-S., Zhao, J., Zhang, K., & Shao, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58621-8_31 · Cited by 213
Mirzaei, G., & Adeli, H. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103293 · Cited by 219
An AIS-based deep learning framework for regional ship behavior prediction
Murray, B., & Perera, L. P. (2021) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2021.107819 · Cited by 223
Pan, S., Zhao, X., & Coelho, L. P. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad209 · Cited by 246
A Hybrid Approach To Hierarchical Density-based Cluster Selection
Malzer, C., & Baum, M. (2020) · IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) · doi:10.1109/mfi49285.2020.9235263 · Cited by 205
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Hassani, K., & Haley, M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00825 · Cited by 198
An overview of clustering methods with guidelines for application in mental health research
Gao, C. X., Dwyer, D. E., Zhu, Y., Smith, C. L., Du, L., Filia, K., Bayer, J., Menssink, J. M., Wang, T., Bergmeir, C., et al. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115265 · Cited by 244
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Sivaraman, G., Krishnamoorthy, A. N., Baur, M., Holm, C., Stan, M., Csányi, G., Benmore, C., & Vázquez-Mayagoitia, Á. (2020) · npj Computational Materials · doi:10.1038/s41524-020-00367-7 · Cited by 194
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Avagyan, S., Henninger, J. E., Mannherz, W. P., Mistry, M., Yoon, J., Yang, S., Weber, M. C., Moore, J. L., & Zon, L. I. (2021) · Science · doi:10.1126/science.aba9304 · Cited by 192
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Schumann, O., Hahn, M., Dickmann, J., & Wöhler, C. (2018) · International Conference on Information Fusion (FUSION) · doi:10.23919/icif.2018.8455344 · Cited by 195
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Basora, L., Olivé, X., & Dubot, T. (2019) · Aerospace · doi:10.3390/aerospace6110117 · Cited by 193
McConnachie, A. W., Ibata, R., Martin, N. F., Ferguson, A. M. N., Collins, M., Gwyn, S., Irwin, M. J., Lewis, G. F., Mackey, D., Davidge, T. J., et al. (2018) · The Astrophysical Journal · doi:10.3847/1538-4357/aae8e7 · Cited by 177
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Djonlagic, I. E., Mariani, S., Fitzpatrick, A. L., van der Klei, V. M. G. T. H., Johnson, D. A., Wood, A. C., Seeman, T. E., Nguyen, H. T., Prerau, M. J., Luchsinger, J. A., et al. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-00964-y · Cited by 184
Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide
Sivaraman, G., Krishnamoorthy, A. N., Baur, M., Holm, C., Stan, M., Cśanyi, G., Benmore, C. J., & Vázquez‐Mayagoitia, Á. (2019) · Cambridge University Engineering Department Publications Database · doi:10.17863/cam.55408 · Cited by 165
Jones, E. M., Lubock, N. B., Venkatakrishnan, A., Wang, J., Tseng, A. M., Paggi, J. M., Latorraca, N. R., Cancilla, D., Satyadi, M., Davis, J. E., et al. (2020) · eLife · doi:10.7554/elife.54895 · Cited by 168
RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter
Mazza, M., Cresci, S., Avvenuti, M., Quattrociocchi, W., & Tesconi, M. (2019) · Proceedings of the 10th ACM Conference on Web Science · doi:10.1145/3292522.3326015 · Cited by 163
The pre-orogenic detrital zircon record of the Peri-Gondwanan crust
Stephan, T., Kroner, U., & Romer, R. L. (2018) · Geological Magazine · doi:10.1017/s0016756818000031 · Cited by 159
Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach
Wang, L., Chen, P., Chen, L., & Mou, J. (2021) · Journal of Marine Science and Engineering · doi:10.3390/jmse9060566 · Cited by 153
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Mou, N., Yuan, R., Yang, T., Zhang, H., Tang, J., & Makkonen, T. (2019) · Tourism Management · doi:10.1016/j.tourman.2019.103955 · Cited by 152
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Thody, S. A., Clements, H. D., Baniasadi, H. R., Lyon, A. S., Sigman, M. S., & Rosen, M. K. (2024) · Nature Chemistry · doi:10.1038/s41557-024-01630-w · Cited by 143
Campello, R. J. G. B., Kröger, P., Sander, J., & Zimek, A. (2019) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1343 · Cited by 142
Self-citationVelde, R. V., Yoon, N., Marusyk, V., Durmaz, A., Dhawan, A., Miroshnychenko, D., Lozano‐Peral, D., Desai, B., Balynska, O. V., Poleszczuk, J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-16212-w · Cited by 141
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Feuerriegel, S., Maarouf, A., Bär, D., Geissler, D., Schweisthal, J., Pröllochs, N., Robertson, C. E., Rathje, S., Hartmann, J., Mohammad, S. M., et al. (2025) · Nature Reviews Psychology · doi:10.1038/s44159-024-00392-z · Cited by 140
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Wang, C., Ji, M., Wang, J., Wen, W., Li, T., & Sun, Y. (2019) · Sensors · doi:10.3390/s19010172 · Cited by 151
Spatiotemporal clustering: a review
Ansari, M. Y., Ahmad, A., Khan, S. S., Bhushan, G., & Mainuddin, M. (2019) · Artificial Intelligence Review · doi:10.1007/s10462-019-09736-1 · Cited by 143
Mowbray, M., Vallerio, M., Pérez-Galván, C., Zhang, D., del Rio‐Chanona, E. A., & Navarro-Brull, F. J. (2022) · Reaction Chemistry & Engineering · doi:10.1039/d1re00541c · Cited by 149
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Katz, H., Lelli, F., McGaugh, S., Cintio, A. D., Brook, C. B., & Schombert, J. M. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw3101 · Cited by 128
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Meingast, S., Alves, J., & Rottensteiner, A. (2020) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202038610 · Cited by 120
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McLaughlin, C. N., Brbić, M., Xie, Q., Li, T., Horns, F., Kolluru, S. S., Kebschull, J. M., Vacek, D., Xie, A., Li, J., et al. (2021) · eLife · doi:10.7554/elife.63856 · Cited by 115
Sánchez-Garrido, A. J., Navarro, I. J., García, J., & Yepes, V. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.106725 · Cited by 131
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Wani, A. A. (2024) · PeerJ Computer Science · doi:10.7717/peerj-cs.2286 · Cited by 168
Queiroz, A. B. A., Anders, F., Chiappini, C., Khalatyan, A., Santiago, B. X., Nepal, S., Steinmetz, M., Gallart, C., Valentini, M., Ponte, M. D., et al. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202245399 · Cited by 131
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Severi, G., Meyer, J., Coull, S. E., & Oprea, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.01031 · Cited by 116
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Derkarabetian, S., Castillo, S., Koo, P. K., Ovchinnikov, S., & Hedin, M. C. (2019) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2019.106562 · Cited by 109
Untangling the Galaxy. II. Structure within 3 kpc
Kounkel, M., Covey, K. R., & Stassun, K. G. (2020) · The Astronomical Journal · doi:10.3847/1538-3881/abc0e6 · Cited by 113
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Rensvold, J. W., Shishkova, E., Sverchkov, Y., Miller, I. J., Çetinkaya, A., Pyle, A., Manicki, M., Brademan, D. R., Alanay, Y., Raiman, J., et al. (2022) · Nature · doi:10.1038/s41586-022-04765-3 · Cited by 106
FL-Defender: Combating targeted attacks in federated learning
Jebreel, N. M., & Domingo-Ferrer, J. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110178 · Cited by 110
Phillips, N. E., Levy, B. L., Sampson, R. J., Small, M. L., & Wang, Q. (2019) · Sociological Methods & Research · doi:10.1177/0049124119852386 · Cited by 119
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Mansouri, M., Önen, M., Jaballah, W. B., & Conti, M. (2023) · Proceedings on Privacy Enhancing Technologies · doi:10.56553/popets-2023-0009 · Cited by 119
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Rice, G., Wagner, T., Stabrin, M., Sitsel, O., Prumbaum, D., & Raunser, S. (2023) · Nature Methods · doi:10.1038/s41592-023-01878-z · Cited by 108
Sánchez‐Franco, M. J., & Rey‐Moreno, M. (2021) · Psychology and Marketing · doi:10.1002/mar.21608 · Cited by 106
Guérin, A., Strelau, K. M., Barylyuk, K., Wallbank, B. A., Berry, L., Crook, O., Lilley, K., Waller, R. F., & Striepen, B. (2023) · Cell Host and Microbe · doi:10.1016/j.chom.2023.03.001 · Cited by 91
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Zhan, S., & Chong, A. (2020) · Energy and Built Environment · doi:10.1016/j.enbenv.2020.08.001 · Cited by 98
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Fricker, H. A., Arndt, P., Brunt, K. M., Datta, R. T., Fair, Z., Jasinski, M. F., Kingslake, J., Magruder, L. A., Moussavi, M. S., Pope, A., et al. (2020) · Geophysical Research Letters · doi:10.1029/2020gl090550 · Cited by 96
Zheng, T., Ghasemi, D. R., Okonechnikov, K., Korshunov, A., Sill, M., Maaß, K. K., da Silva, P. B. G., Ryzhova, M. V., Gojo, J., Stichel, D., et al. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-20-0963 · Cited by 94
Delta-density based clustering with a divide-and-conquer strategy: 3DC clustering
Zhou, L., & Chen, P. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.01.009 · Cited by 93
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Ripan, R. C., Sarker, I. H., Hossain, S. M. M., Anwar, M. M., Nowrozy, R., Hoque, M. M., & Furhad, M. H. (2021) · SN Computer Science · doi:10.1007/s42979-021-00518-7 · Cited by 99
Zhang, Y., Chen, Z., Zheng, X., Chen, N., & Wang, Y. (2021) · Journal of Hydrology · doi:10.1016/j.jhydrol.2021.127053 · Cited by 90
Gao, Z., Zeng, H., Zhang, X., Wu, H., Zhang, R., Sun, Y., Du, Q., Zhao, Z., Li, Z., Zhao, F., et al. (2024) · International Journal of Digital Earth · doi:10.1080/17538947.2024.2310723 · Cited by 92
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García, J., Villavicencio, G. E., Altimiras, F. J., Crawford, B., Soto, R., Minatogawa, V., Franco, M., Martínez-Muñoz, D., & Yepes, V. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104532 · Cited by 96
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Zhang, D., Lee, K., & Lee, I. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.09.040 · Cited by 86
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Fekry, R., Yao, W., Cao, L., & Shen, X. (2022) · Forest Ecosystems · doi:10.1016/j.fecs.2022.100065 · Cited by 85
Metadata
- Authors
- Campello, R. J. G. B., Moulavi, D., & Sander, J.
- Affiliations
- University of Alberta (CA)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Volume
- 7819
- Pages
- 160-172
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- 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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