A Cluster Separation Measure

<b>NbClust</b> : An <i>R</i> Package for Determining the Relevant Number of Clusters in a Data Set

Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A. (2014) · Journal of Statistical Software · doi:10.18637/jss.v061.i06 · Cited by 3,396 · see citing works

Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis

Farrell, J. A., Wang, Y., Riesenfeld, S. J., Shekhar, K., Regev, A., & Schier, A. F. (2018) · Science · doi:10.1126/science.aar3131 · Cited by 876 · see citing works

Chronic, multisite, multielectrode recordings in macaque monkeys

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Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex

Luo, C., Keown, C. L., Kurihara, L., Zhou, J., He, Y., Li, J., Castanon, R., Lucero, J., Nery, J. R., Sandoval, J. P., et al. (2017) · Science · doi:10.1126/science.aan3351 · Cited by 545 · see citing works

EMG feature evaluation for movement control of upper extremity prostheses

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A deep look into radiomics

Scapicchio, C., Gabelloni, M., Barucci, A., Cioni, D., Saba, L., & Neri, E. (2021) · La radiologia medica · doi:10.1007/s11547-021-01389-x · Cited by 435 · see citing works

Quantifying controversy on social media

Garimella, K., Morales, G. D. F., Gionis, A., & Mathioudakis, M. (2018) · ACM Transactions on Social Computing · doi:10.1145/3140565 · Cited by 348 · see citing works

Heterogeneous organization of the locus coeruleus projections to prefrontal and motor cortices

Chandler, D. J., Gao, W.-J., & Waterhouse, B. D. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320827111 · Cited by 341 · see citing works

Occupancy schedules learning process through a data mining framework

D’Oca, S., & Hong, T. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2014.11.065 · Cited by 294 · see citing works

Longitudinal Study of Postconcussion Syndrome: Not Everyone Recovers

Hiploylee, C., Dufort, P. A., Davis, H. S., Wennberg, R. A., Tartaglia, M. C., Mikulis, D., Hazrati, L.-N., & Tator, C. H. (2017) · Journal of Neurotrauma · doi:10.1089/neu.2016.4677 · Cited by 293 · see citing works

Utilities for quantifying separation in PCA/PLS-DA scores plots

Worley, B., Halouska, S., & Powers, R. (2013) · Analytical Biochemistry · doi:10.1016/j.ab.2012.10.011 · Cited by 214 · see citing works

Real-time scheduling for a smart factory using a reinforcement learning approach

Shiue, Y.-R., Lee, K.-C., & Su, C.-T. (2018) · Computers &amp; Industrial Engineering · doi:10.1016/j.cie.2018.03.039 · Cited by 225 · see citing works

Insights on strategic air taxi network infrastructure locations using an iterative constrained clustering approach

Rajendran, S., & Zack, J. (2019) · Transportation Research Part E: Logistics and Transportation Review · doi:10.1016/j.tre.2019.06.003 · Cited by 156 · see citing works

Quantifying Controversy in Social Media

Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M. (2016) · Proceedings of the Ninth ACM International Conference on Web Search and Data Mining · doi:10.1145/2835776.2835792 · Cited by 147 · see citing works

Visual analytics in urban computing: An overview

Zheng, Y., Wu, W., Chen, Y., Qu, H., & Ni, L. M. (2016) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2016.2586447 · Cited by 141 · see citing works

Cluster Analysis of Smart Metering Data: An Implementation in Practice

Flath, C., Nicolay, D., Conte, T., van Dinther, C., & Filipova-Neumann, L. (2012) · Business &amp; Information Systems Engineering · doi:10.1007/s12599-011-0201-5 · Cited by 137 · see citing works

<i>K</i>-Means Clustering-Based Kernel Canonical Correlation Analysis for Multimodal Emotion Recognition in Human–Robot Interaction

Chen, L., Wang, K., Li, M., Wu, M., Pedrycz, W., & Hirota, K. (2023) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2022.3150097 · Cited by 113 · see citing works

Having Beer after Prayer? Measuring Cultural Bias in Large Language Models

Naous, T., Ryan, M. J., Ritter, A., & Xu, W. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.862 · Cited by 96 · see citing works

A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy techniques

Micera, S., Sabatini, A. M., Dario, P., & Rossi, B. (1999) · Medical Engineering &amp; Physics · doi:10.1016/s1350-4533(99)00055-7 · Cited by 90 · see citing works

A statistical method for build orientation determination in additive manufacturing

Zhang, Y., Harik, R., Fadel, G., & Bernard, A. (2019) · Rapid Prototyping Journal · doi:10.1108/rpj-04-2018-0102 · Cited by 63 · see citing works

Machine learning in the clinical and language characterisation of primary progressive aphasia variants

Matias-Guiu, J. A., Díaz-Álvarez, J., Cuetos, F., Cabrera-Martín, M. N., Segovia-Ríos, I., Pytel, V., Moreno-Ramos, T., Carreras, J. L., Matías-Guiu, J., & Ayala, J. L. (2019) · Cortex · doi:10.1016/j.cortex.2019.05.007 · Cited by 50 · see citing works

Using ontology-based clustering to understand the push and pull factors for British tourists visiting a Mediterranean coastal destination

Valls, A., Gibert, K., Orellana, A., & Antón-Clavé, S. (2018) · Information &amp; Management · doi:10.1016/j.im.2017.05.002 · Cited by 50 · see citing works

Fuel prices and the invention crowding out effect: Releasing the automotive industry from its dependence on fossil fuel

Barbieri, N. (2016) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.07.002 · Cited by 43 · see citing works

Building energy retrofit index for policy making and decision support at regional and national scales

Khayatian, F., Sarto, L., & Dall'O', G. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.08.237 · Cited by 40 · see citing works

Identification of a two-layer regulatory network of proliferation-related microRNAs in hepatoma cells

Huang, Y., Chen, H.-C., Chiang, C.-W., Yeh, C.-T., Chen, S.-J., & Chou, C.-K. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks789 · Cited by 37 · see citing works

Workflow for generating competing hypothesis from models with parameter uncertainty

Gomez-Cabrero, D., Compte, A., & Tegner, J. (2011) · Interface Focus · doi:10.1098/rsfs.2011.0015 · Cited by 33 · see citing works

Retinotopic to spatiotopic mapping in blind patients implanted with the Argus II retinal prosthesis

Caspi, A., Roy, A., Dorn, J. D., & Greenberg, R. J. (2017) · Investigative Opthalmology &amp; Visual Science · doi:10.1167/iovs.16-20398 · Cited by 32 · see citing works

Affect-aware behaviour modelling and control inside an intelligent environment

Leon, E., Clarke, G., Callaghan, V., & Doctor, F. (2010) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2009.12.002 · Cited by 30 · see citing works

Trends in Glucagon-Like Peptide-1 Receptor Agonist Social Media Posts Using Artificial Intelligence

Javaid, A., Baviriseaty, S., Javaid, R., Zirikly, A., Kukreja, H., Kim, C. H., Blaha, M. J., Blumenthal, R. S., Martin, S. S., & Marvel, F. A. (2024) · JACC: Advances · doi:10.1016/j.jacadv.2024.101182 · Cited by 24 · see citing works

Bioinformatics Analyses Determined the Distinct CNS and Peripheral Surrogate Biomarker Candidates Between Two Mouse Models for Progressive Multiple Sclerosis

Omura, S., Sato, F., Martinez, N. E., Park, A.-M., Fujita, M., Kennett, N. J., Cvek, U., Minagar, A., Alexander, J. S., & Tsunoda, I. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.00516 · Cited by 20 · see citing works

LSISOM — A Latent Semantic Indexing Approach to Self-Organizing Maps of Document Collections

Ampazis, N., & Perantonis, S. J. (2004) · Neural Processing Letters · doi:10.1023/b:nepl.0000023449.95030.8f · Cited by 18 · see citing works

Exploring the orientations which characterise the likely public acceptance of low emission energy technologies

Carr-Cornish, S., Ashworth, P., Gardner, J., & Fraser, S. J. (2011) · Climatic Change · doi:10.1007/s10584-010-9964-9 · Cited by 12 · see citing works

ICAT: a novel algorithm to robustly identify cell states following perturbations in single-cell transcriptomes

Hawkins, D. Y., Zuch, D. T., Huth, J., Rodriguez-Sastre, N., McCutcheon, K. R., Glick, A., Lion, A. T., Thomas, C. F., Descoteaux, A. E., Johnson, W. E., et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad278 · Cited by 10 · see citing works

Identifying Features that Shape Perceived Consciousness in Large Language Model-based AI: A Quantitative Study of Human Responses

Kang, B., Kim, J., Yun, T.-R., Bae, H., & Kim, C.-E. (2025) · arXiv · doi:10.48550/arxiv.2502.15365 · Cited by 6

Identifying features that shape perceived consciousness in LLM-based AI: A quantitative study of human responses

Kang, B., Kim, J., Yun, T., Bae, H., & Kim, C.-E. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100901 · Cited by 6 · see citing works

Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases

Zheng, Y., Schupp, J., Adams, T., Clair, G., Justet, A., Ahangari, F., Yan, X., Hansen, P., Carlon, M., Cortesi, E., et al. (2023) · doi:10.21203/rs.3.rs-3676579/v1 · Cited by 34 · see citing works

Sustainability of Online Teaching: The Case Study Mother Tongue Spelling Course at Montenegrin Universities

Buric, M., Novovic Buric, M., Jaksic Stojanovic, A., Kascelan, L., & Zugić, D. (2022) · Sustainability · doi:10.3390/su142113717 · Cited by 3 · see citing works

Clustering of Temporal and Visual Data: Recent Advancements

Mudgal, P. (2026) · Data · doi:10.3390/data11010007 · Cited by 2 · see citing works

Visitation patterns across mobility groups: wandering, commuting, and exploring

Hołubowska, O., Smolak, K., Brum-Bastos, V., & Sila-Nowicka, K. (2026) · Journal of Geographical Systems · doi:10.1007/s10109-026-00501-x

Cross-robot behavior adaptation through intention alignment

Chen, X., Gao, Y., Liu, H., Yang, F., Ghadirzadeh, A., Yang, J., Liang, B., Zhang, C., Lam, T. L., & Zhu, S.-C. (2026) · Science Robotics · doi:10.1126/scirobotics.adv2250