The DOI matches its Crossref record; 26 of the 27 references it cites were found in a registry or an index.
Density-Based Clustering Based on Hierarchical Density Estimates
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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]
Citations
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
Cited by
Sorted by citations
Items 501–600 of 1,003
Aytaç, E., & Khayet, M. (2023) · Separations · doi:10.3390/separations10090482 · Cited by 19
Karabacak, M., Jagtiani, P., Jain, A., Panov, F. E., & Margetis, K. D. (2024) · Epilepsia · doi:10.1111/epi.17890 · Cited by 21
Mask4Former: Mask Transformer for 4D Panoptic Segmentation
Yilmaz, K., Schult, J., Nekrasov, A., & Leibe, B. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610262 · Cited by 22
A Hybrid Framework for News Clustering Based on the DBSCAN-Martingale and LDA
Gialampoukidis, I., Vrochidis, S., & Kompatsiaris, Y. (2016) · IAPR International Conference on Machine Learning and Data Mining in Pattern Recognition · doi:10.1007/978-3-319-41920-6_13 · Cited by 23
An improved density peaks method for data clustering
Lotfi, A., Seyedi, S. A., & Moradi, P. (2016) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke.2016.7802150 · Cited by 19
Chen, Y., Sun, E. W., & Lin, Y. (2019) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.02.046 · Cited by 19
Adjutant: an R-based tool to support topic discovery for systematic and literature reviews
Crisan, A., Munzner, T., & Gardy, J. L. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty722 · Cited by 19
A Python Toolbox for Processing Air Traffic Data: A Use Case with Trajectory Clustering
Olivé, X., & Basora, L. (2019) · EPiC series in computing · doi:10.29007/sf1f · Cited by 19
SMAC-Seg: LiDAR Panoptic Segmentation via Sparse Multi-directional Attention Clustering
Li, E., Razani, R., Xu, Y., & Liu, B. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9812408 · Cited by 19
Efficient Computation of Multiple Density-Based Clustering Hierarchies
Neto, A. C. A., Sander, J., Campello, R. J. G. B., & Nascimento, M. A. (2017) · Proceedings · doi:10.1109/icdm.2017.127 · Cited by 19
Self-citationDetecting taxi movements using Random Swap clustering and sequential pattern mining
Ibrahim, R., & Shafiq, M. O. (2019) · Journal Of Big Data · doi:10.1186/s40537-019-0203-6 · Cited by 19
Thirty years of climate change research: A fine-grained analysis of geographical specialization
Debernardi, C., Seeber, M., & Cattaneo, M. (2024) · Environmental Science & Policy · doi:10.1016/j.envsci.2023.103663 · Cited by 18
Lai, Q., Xin, Q., Tian, Y., Chen, X., Li, Y., & Wu, R. (2025) · Remote Sensing · doi:10.3390/rs17020341 · Cited by 18
Discovering generalized clusters with adaptive mixture density-based clustering
Fei, Z., Zhai, H., Yang, J., Wang, B., & Ma, Y. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113250 · Cited by 18
Jeon, H., Lee, H., Kuo, Y.-H., Yang, T., Archambault, D., Ko, S., Fujiwara, T., Ma, K., & Seo, J. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713551 · Cited by 18
SuperSalt: equivariant neural network force fields for multicomponent molten salts system
Shen, C., Attarian, S., Zhang, Y., Zhang, H., Asta, M., Szlufarska, I., & Morgan, D. D. (2025) · Nature Communications · doi:10.1038/s41467-025-62450-1 · Cited by 28
Star Cluster Ages in the Gaia Era
Choi, J., Conroy, C., Ting, Y.-S., Cargile, P. A., Dotter, A., & Johnson, B. D. (2018) · The Astrophysical Journal · doi:10.3847/1538-4357/aad18c · Cited by 18
Data-Driven Classification of Coronal Hole and Streamer Belt Solar Wind
Bloch, T., Watt, C. E. J., Owens, M. J., McInnes, L., & Macneil, A. (2020) · Solar Physics · doi:10.1007/s11207-020-01609-z · Cited by 18
Distilling Multiple Domains for Neural Machine Translation
Currey, A., Mathur, P., & Dinu, G. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.364 · Cited by 21
Martin, W., Sheynkman, G., Lightstone, F. C., Nussinov, R., & Cheng, F. (2021) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2021.09.001 · Cited by 20
Significant DBSCAN+: Statistically Robust Density-based Clustering
Xie, Y., Jia, X., Shekhar, S., Bao, H., & Zhou, X. S. (2021) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3474842 · Cited by 18
A Density-Based Spatial Cluster Analysis Supporting The Building Stock Analysis In Historical Towns
Lucchi, E., D’Alonzo, V., Exner, D., Zambelli, P., & Garegnani, G. (2020) · Building Simulation Conference proceedings · doi:10.26868/25222708.2019.210346 · Cited by 18
A hierarchical graph-based model for mobility data representation and analysis
Elayam, M. M., Ray, C., & Claramunt, C. (2022) · Data & Knowledge Engineering · doi:10.1016/j.datak.2022.102054 · Cited by 18
BERT-based Topic Modeling Approach for Malaria Research Publication
Hidayat, A. A., Nirwantono, R., Budiarto, A., & Pardamean, B. (2022) · International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) · doi:10.1109/icimcis56303.2022.10017743 · Cited by 19
Automatic detection of spatio-temporal signaling patterns in cell collectives
Gagliardi, P. A., Grädel, B., Jacques, M., Hinderling, L., Ender, P., Cohen, A. R., Kastberger, G., Pertz, O. C., & Dobrzyński, M. (2023) · The Journal of Cell Biology · doi:10.1083/jcb.202207048 · Cited by 20
Niroomand, K., Saady, N. M. C., Bazán, C., Zendehboudi, S., Soares, A., & Albayati, T. M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106848 · Cited by 18
Spadar, A., Collins, E. L., Messenger, L. A., Clark, T. G., & Campino, S. G. (2024) · Scientific Reports · doi:10.1038/s41598-024-64007-6 · Cited by 18
Fu, H., Qiu, L., Ai, Y., Tu, J., & Yan, Y. (2024) · Electrical Engineering · doi:10.1007/s00202-024-02738-5 · Cited by 22
LiDAR target detection and classification for ship situational awareness: A hybrid learning approach
Ponzini, F., Zaccone, R., & Martelli, M. (2025) · Applied Ocean Research · doi:10.1016/j.apor.2025.104552 · Cited by 20
A Gaussian Process Regression for Natural Gas Consumption Prediction Based on Time Series Data
Laib, O., Khadir, M. T., & Mihaylova, L. S. (2018) · International Conference on Information Fusion (FUSION) · doi:10.23919/icif.2018.8455447 · Cited by 18
DoCA: A Content-Based Automatic Classification System Over Digital Documents
Eken, S., Menhour, H., & Köksal, K. (2019) · IEEE Access · doi:10.1109/access.2019.2930339 · Cited by 18
A Density Peak Clustering Algorithm Based on the K‐Nearest Shannon Entropy and Tissue‐Like P System
Jiang, Z., Liu, X., & Sun, M. (2019) · Mathematical Problems in Engineering · doi:10.1155/2019/1713801 · Cited by 18
Toh, A., Chen, W. J., Takeuchi, N., Dreger, D. S., Chi, W., & Ide, S. (2020) · Geophysical Research Letters · doi:10.1029/2020gl087244 · Cited by 18
Rapid sorting of radio galaxy morphology using Haralick features
Ntwaetsile, K., & Geach, J. E. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab271 · Cited by 18
Dynamics of fabric and dryer sheet motion in domestic clothes dryers
Jones, C. R., Corona, A., Amador, C., & Fryer, P. J. (2021) · Drying Technology · doi:10.1080/07373937.2021.1918706 · Cited by 18
Keren, R., Méheust, R., Santini, J. M., Thomas, A. D., West-Roberts, J. A., Banfield, J. F., & Alvarez‐Cohen, L. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.12.040 · Cited by 18
Hierarchical Clustering Given Confidence Intervals of Metric Distances
Huang, W., & Ribeiro, A. (2018) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2018.2813322 · Cited by 18
Du, X., Wang, Z., & Wang, Y. (2023) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi12080321 · Cited by 17
FedMP: A multi-pronged defense algorithm against Byzantine poisoning attacks in federated learning
Zhao, K., Wang, L., Yu, F., Zeng, B., & Pang, Z. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110990 · Cited by 19
$k$-Graph: A Graph Embedding for Interpretable Time Series Clustering
Boniol, P., Tiano, D., Bonifati, A., & Palpanas, T. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3543946 · Cited by 17
Sheng, H., Huang, Z., Ke, L., Zhang, J., Zeng, Z., Yasir, M., & Liu, S. (2025) · Ocean Engineering · doi:10.1016/j.oceaneng.2025.121461 · Cited by 17
Paving the Roadmap for XAI and IML in Healthcare: Data-Driven Discoveries and the FIXAIH Framework
Alghamdi, S., Mehmood, R., Alqurashi, F. A., & Alzahrani, A. (2025) · IEEE Access · doi:10.1109/access.2025.3616353 · Cited by 22
FBA: feature barcoding analysis for single cell RNA-Seq
Duan, J., & Hon, G. C. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab375 · Cited by 17
Bussola, N., Papa, B., Melaiu, O., Castellano, A., Fruci, D., & Jurman, G. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22168804 · Cited by 16
Canis Major OB1 stellar group contents revealed by Gaia
Santos-Silva, T., Perottoni, H. D., Almeida-Fernandes, F., Gregorio‐Hetem, J., Jatenco‐Pereira, V., de Oliveira, C. M., Montmerle, T., Bica, E., Bonatto, C., Monteiro, H. S. A., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab2409 · Cited by 17
Hybrid Network–Spatial Clustering for Optimizing 5G Mobile Networks
Margaris, A., Filippas, I., & Tsagkaris, K. A. (2022) · Applied Sciences · doi:10.3390/app12031203 · Cited by 17
Deep flight track clustering based on spatial–temporal distance and denoising auto-encoding
Liu, G., Fan, Y., Zhang, J., Wen, P., Lyu, Z., & Yuan, X. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116733 · Cited by 17
Predictive distance-based road pricing — Designing tolling zones through unsupervised learning
Lentzakis, A. F., Seshadri, R., & Ben-Akiva, M. E. (2023) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2023.103611 · Cited by 17
Adreani, L., Bellini, P., Fanfani, M., Nesi, P., & Pantaleo, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-37126-4_34 · Cited by 17
Deep learning for COVID-19 topic modelling via Twitter: Alpha, Delta and Omicron
Lande, J., Pillay, A. A., & Chandra, R. (2023) · PLoS ONE · doi:10.1371/journal.pone.0288681 · Cited by 18
Chen, R. Q., Joffe, B., Costa, P. C., Filan, C. E., Wang, B., Balakirsky, S. B., Robles, F. E., Roy, K., & Li, J. (2023) · Cytotherapy · doi:10.1016/j.jcyt.2023.08.011 · Cited by 17
Geeraerts, D., Speelman, D., Heylen, K., Montes, M., Pascale, S. D., Franco, K., & Lang, M. (2023) · doi:10.1093/oso/9780198890676.001.0001 · Cited by 23
No Longer Trending on Artstation: Prompt Analysis of Generative AI Art
McCormack, J., Llano, M. T., Krol, S. J., & Rajcic, N. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56992-0_18 · Cited by 28
May, D., Woodhouse, S., Noakes, M. T., DOROSCHAK, K., Elyanow, R., Moore, A. J., Osborne, E. J., Greenfield, B., Hayes, T., Taniguchi, R. T., et al. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1944643 · Cited by 20
Data depth based clustering analysis
Jeong, M., Cai, Y., Sullivan, C. J., & Wang, S. (2016) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2996913.2996984 · Cited by 17
ConcaveCubes: Supporting Cluster‐based Geographical Visualization in Large Data Scale
Li, M., Choudhury, F. M., Bao, Z., Samet, H., & Sellis, T. (2018) · Computer Graphics Forum · doi:10.1111/cgf.13414 · Cited by 17
Fingerprints and Floor Plans Construction for Indoor Localisation Based on Crowdsourcing
Santos, R., Barandas, M., Leonardo, R., & Gamboa, H. F. S. (2019) · Sensors · doi:10.3390/s19040919 · Cited by 17
Unsupervised learning for detection of mobility related anomalies in commercial LTE networks
Moysen, J., Ahmed, F., García‐Lozano, M., & Niemelä, J. (2020) · European Conference on Networks and Communications · doi:10.1109/eucnc48522.2020.9200970 · Cited by 17
Lin, L., Guo, J., Aqeel, M. M., Gelfand, S. B., Delp, E. J., Bhadra, A., Richards, E. A., Hennessy, E., & Eicher‐Miller, H. A. (2021) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqab339 · Cited by 17
A Novel Hierarchical Clustering Algorithm Based on Density Peaks for Complex Datasets
Zhou, R., Zhang, Y., Feng, S., & Luktarhan, N. (2018) · Complexity · doi:10.1155/2018/2032461 · Cited by 17
Corrales, M., Berti, S., Denel, B., Williamson, P., Aleardi, M., & Ravasi, M. (2025) · Geophysical Journal International · doi:10.1093/gji/ggaf096 · Cited by 16
Discovery of liquid crystalline polymers with high thermal conductivity using machine learning
Maeda, H., Wu, S., Marui, R., Yoshida, E., Hatakeyama‐Sato, K., Nabae, Y., Nakagawa, S., Ryu, M., Ishige, R., Noguchi, Y., et al. (2025) · npj Computational Materials · doi:10.1038/s41524-025-01671-w · Cited by 22
Data Association and Localization of Classified Objects in Visual SLAM
Iqbal, A., & Gans, N. (2020) · Journal of Intelligent & Robotic Systems · doi:10.1007/s10846-020-01189-x · Cited by 16
Malki, N. E., Cugny, R., Teste, O., & Ravat, F. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3412125 · Cited by 16
Seebacher, D., Polk, T., Janetzko, H., Keim, D. A., Schreck, T., & Stein, M. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3134814 · Cited by 16
Preiss, A., Baumgartner, P., Edlund, M. J., & Bobashev, G. (2022) · JMIR Formative Research · doi:10.2196/33919 · Cited by 16
Q-Chef: The impact of surprise-eliciting systems on food-related decision-making
Grace, K. S., Finch, E., Gulbransen-Diaz, N., & Henderson, H. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3501862 · Cited by 16
Visual Comparison of Language Model Adaptation
Sevastjanova, R., Cakmak, E., Ravfogel, S., Cotterell, R., & El‐Assady, M. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3209458 · Cited by 17
Improved visualization of high-dimensional data using the distance-of-distance transformation
Liu, J., & Vinck, M. A. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010764 · Cited by 15
The Gaia -ESO Survey: Chemical evolution of Mg and Al in the Milky Way with machine learning
Ambrosch, M., Guiglion, G., Mikolaitis, Š., Chiappini, C., Tautvaišienė, G., Nepal, S., Gilmore, G. F., Randich, S., Bensby, T., Bayo, A., et al. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202244766 · Cited by 16
Zhou, P., Zhang, T., Zhao, L.-W., Qi, Y., Chang, Y., & Bai, L. (2023) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2023.106382 · Cited by 14
Rey‐Moreno, M., Sánchez‐Franco, M. J., & Rey-Tienda, S. (2023) · Tourism & Management Studies · doi:10.18089/tms.2023.190202 · Cited by 19
Topic Modeling-Based Framework for Extracting Marketing Information From E-Commerce Reviews
An, Y., Kim, D., Lee, J.-Y., Oh, H., Lee, J.-S., & Jeong, D. (2023) · IEEE Access · doi:10.1109/access.2023.3337808 · Cited by 16
UMAP for Geospatial Data Visualization
de Zarzà, I., de Curtò, J., & Calafate, C. T. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.10.155 · Cited by 16
Population encoding of stimulus features along the visual hierarchy
Dyballa, L., Rudzite, A. M., Hoseini, M. S., Thapa, M., Stryker, M. P., Field, G. D., & Zucker, S. W. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2317773121 · Cited by 19
Fraud Detection in Credit Card Transactions Using HDBSCAN, UMAP and SMOTE Methods
Setiawan, R., Tjahjono, B., Firmansyah, G., & Akbar, H. (2023) · International Journal Of Science Technology & Management · doi:10.46729/ijstm.v4i5.929 · Cited by 16
Identification of typical district configurations: A two-step global sensitivity analysis framework
Chuat, A., Terrier, C., Schnidrig, J., & Marechal, F. M. A. (2024) · Energy · doi:10.1016/j.energy.2024.131116 · Cited by 16
Leppänen, P.-K., Kinnunen, A., Hautamäki, R., Järvi, L., Havu, M., Junnila, S., & Tahvonen, O. (2024) · Urban forestry & urban greening · doi:10.1016/j.ufug.2024.128343 · Cited by 16
Novel concept-based image captioning models using LSTM and multi-encoder transformer architecture
Osman, A. A. E., Shalaby, M. A. W., Soliman, M., & Elsayed, K. M. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-69664-1 · Cited by 19
A computational method to reveal psychological constructs from text data.
Herderich, A., Freudenthaler, H. H., & García, D. (2024) · Psychological Methods · doi:10.1037/met0000700 · Cited by 20
Perron, U., Grassi, E., Chatzipli, A., Viviani, M., Karakoç, E., Trastulla, L., Brochier, L. M., Isella, C., Zanella, E. R., Klett, H., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-53163-y · Cited by 17
Localization of Classified Objects in SLAM using Nonparametric Statistics and Clustering
Iqbal, A., & Gans, N. (2018) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8593541 · Cited by 16
Dynamic Mode Decomposition Analysis of Spatially Agglomerated Flow Databases
Li, B., Mena, J. G., Zheng, Y., & Valero, E. (2020) · Energies · doi:10.3390/en13092134 · Cited by 16
Murray, B., & Perera, L. P. (2021) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2021.10.067 · Cited by 16
Phrase Embedding and Clustering for Sub-Feature Extraction From Online Data
Park, S., & Kim, H. (2021) · Journal of Mechanical Design · doi:10.1115/1.4052904 · Cited by 16
Conformational Landscapes of Halohydrin Dehalogenases and Their Accessible Active Site Tunnels
Estévez‐Gay, M., Iglesias‐Fernández, J., & Osuna, S. (2020) · Catalysts · doi:10.3390/catal10121403 · Cited by 16
Requena, D., Medico, J. A., Soto, L. F., Shirani, M., Saltsman, J. A., Torbenson, M. S., Coffino, P., & Simon, S. M. (2024) · Nature Communications · doi:10.1038/s41467-024-55238-2 · Cited by 17
Using BERTopic modelling to map the evolution of space syntax research
Mohamed, A. A. (2025) · Land Use Policy · doi:10.1016/j.landusepol.2025.107639 · Cited by 17
Anomaly detection in ship trajectories using machine learning and dynamic time warping
Mangé, V., Tourneret, J., Vincent, F., Mirambell, L., & Vieira, F. M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111185 · Cited by 15
Classification of clusters in collision cascades
Bhardwaj, U., Sand, A. E., & Warrier, M. K. (2019) · Computational Materials Science · doi:10.1016/j.commatsci.2019.109364 · Cited by 15
Modeling binary and graded cone cell fate patterning in the mouse retina
Eldred, K. C., Avelis, C. M., Johnston, R. J., & Roberts, E. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007691 · Cited by 15
Samani, E., Kohansal, M., & Mohsenian‐Rad, H. (2021) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2021.3114362 · Cited by 15
What are the Latest Cybersecurity Trends? A Case Study Grounded in Language Models
Vladescu, C., Dinisor, M.-A., Grigorescu, O., Corlatescu, D., Sandescu, C., & Dascălu, M. (2021) · International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) · doi:10.1109/synasc54541.2021.00033 · Cited by 16
Koester, A. M., Geiser, A., Bowman, P. R. T., van de Linde, S., Gadegaard, N., Bryant, N. J., & Gould, G. W. (2022) · Scientific Reports · doi:10.1038/s41598-022-24736-y · Cited by 16
Carrasco, C. A., & Hernández-del-Valle, A. (2023) · Journal of Environmental Management · doi:10.1016/j.jenvman.2023.117840 · Cited by 15
Active source zones and earthquake vulnerability around Sumatra subduction zone
Rai, A. K., Malakar, S., & Goswami, S. (2023) · Journal of Earth System Science · doi:10.1007/s12040-023-02070-9 · Cited by 15
Oliveira, L. T., Kuffer, M., Schwarz, N., & Pedrassoli, J. C. (2023) · European Journal of Remote Sensing · doi:10.1080/22797254.2023.2214690 · Cited by 15
Şenol, A. (2023) · Acta Polytechnica Hungarica · doi:10.12700/aph.21.2.2024.2.6 · Cited by 15
Martini 3 Force Field Parameters for Protein Lipidation Post-Translational Modifications
Koukos, P. I., Dehghani‐Ghahnaviyeh, S., Velez‐Vega, C., Manchester, J. I., Tieleman, D. P., Duca, J. S., Souza, P. C. T., & Cournia, Z. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00604 · Cited by 16
Comparative study on chromatin loop callers using Hi-C data reveals their effectiveness
Chowdhury, H. M. A. M., Boult, T. E., & Oluwadare, O. (2023) · bioRxiv · doi:10.1186/s12859-024-05713-w · Cited by 12
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review