When Is ''Nearest Neighbor'' Meaningful?

Basic local alignment search tool

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Color indexing

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Multivariate Density Estimation

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Texture features for browsing and retrieval of image data

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Adaptive Control Processes: A Guided Tour

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Efficient similarity search in sequence databases

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Visual learning and recognition of 3-d objects from appearance

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Using discriminant eigenfeatures for image retrieval

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Efficient and effective Querying by Image Content

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V. Adaptive Control Processes

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Nearest Neighbor Classifier Based on Nearest Feature Decisions

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Niching foundations

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Incorporating unsupervised learning in activity recognition

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Fine-grained document clustering via ranking and its application to social media analytics

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Combining semantic and term frequency similarities for text clustering

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Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

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Curse of Dimensionality in Pivot Based Indexes

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Content and Structure Based Approach For XML Similarity

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ProGreSS: SIMULTANEOUS SEARCHING OF PROTEIN DATABASES BY SEQUENCE AND STRUCTURE

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The Categorization Experiment: Experimental Design and Data Analysis

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Analysis and Exploitation of Multipath Ghosts in Radar Target Image Classification

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Sanitizing and measuring privacy of large sparse datasets for recommender systems

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Impact of metrics on biclustering solution and quality: A review

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Evaluation of Improvements in the Separation of Monolayer and Multilayer Films via Measurements in Transflection and Application of Machine Learning Approaches

Koinig, G., Kuhn, N., Barretta, C., Friedrich, K., & Vollprecht, D. (2022) · Polymers · doi:10.3390/polym14193926 · Cited by 20

How does the structure of data impact cell–cell similarity? Evaluating how structural properties influence the performance of proximity metrics in single cell RNA-seq data

Watson, E. R., Mora, A., Fard, A. T., & Mar, J. C. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac387 · Cited by 22

Channel Charting Aided Pilot Reuse for Massive MIMO Systems With Spatially Correlated Channels

Ribeiro, L., Leinonen, M., Al–Tous, H., Tirkkonen, O., & Juntti, M. J. (2022) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2022.3225054 · Cited by 20

Harnessing graph neural networks to craft fragrances based on consumer feedback

Rodrigues, B. C. L., Santana, V. V., Queiroz, L. P., Rebello, C. M., & Nogueira, I. B. R. (2024) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2024.108674 · Cited by 20

Band Depth Clustering for Nonstationary Time Series and Wind Speed Behavior

Tupper, L. L., Matteson, D. S., Anderson, C. L., & Zéphyr, L. (2017) · Technometrics · doi:10.1080/00401706.2017.1345700 · Cited by 20

A clustering algorithm for sample data based on environmental pollution characteristics

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Data Visualization and Feature Selection Methods in Gel-based Proteomics

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Distributed approximate spectral clustering for large-scale datasets

Hefeeda, M. M., Gao, F., & AbdAlmageed, W. (2012) · ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC) · doi:10.1145/2287076.2287111 · Cited by 20

WIND SPEED FORECASTING USING REPTREE AND BAGGING METHODS IN KIRKLARELI-TURKEY

Zontul, M., Aydın, F., Dogan, G., Sener, S., & Kaynar, O. (2013) · Kırklareli University Institutional Repository (Kırklareli University) · Cited by 20

Feature-based cluster validation for high-dimensional data

Kassab, R., & Lamirel, J.-C. (2008) · HAL · Cited by 20

A vibration-based method for contact pattern assessment in straight bevel gears

Buzzoni, M., D’Elia, G., Mucchi, E., & Dalpiaz, G. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.10.002 · Cited by 20

Performance Evaluation of Features and Clustering Algorithms for Malware

Faridi, H., Srinivasagopalan, S., & Verma, R. M. (2018) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2018.00010 · Cited by 20

Cluster Analysis of Microarray Data

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Data-driven characterization of molecular phenotypes across heterogeneous sample collections

Mehtonen, J., Pölönen, P., Häyrynen, S., Dufva, O., Lin, J., Liuksiala, T., Granberg, K. J., Lohi, O., Hautamäki, V., Nykter, M., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz281 · Cited by 20

Pan-Genomic and Polymorphic Driven Prediction of Antibiotic Resistance in Elizabethkingia

Naidenov, B., Willyerd, K. L., Lim, A., Torres, N. J., Johnson, W. L., Hwang, H. J., Hoyt, P. R., Gustafson, J. E., & Chen, C. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.01446 · Cited by 20

Subspace Clustering Without Knowing the Number of Clusters: A Parameter Free Approach

Menon, V., Muthukrishnan, G., & Kalyani, S. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.3018665 · Cited by 20

A comparative study of data-dependent approaches without learning in measuring similarities of data objects

Aryal, S., Ting, K. M., Washio, T., & Haffari, G. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00660-0 · Cited by 20

CFOF

Angiulli, F. (2020) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3362158 · Cited by 20

Instability results for Euclidean distance, nearest neighbor search on high dimensional Gaussian data

Giannella, C. R. (2021) · Information Processing Letters · doi:10.1016/j.ipl.2021.106115 · Cited by 20

Matrix Profile XXIII: Contrast Profile: A Novel Time Series Primitive that Allows Real World Classification

Mercer, R., Alaee, S., Abdoli, A., Singh, S., Murillo, A. C., & Keogh, E. (2021) · Proceedings · doi:10.1109/icdm51629.2021.00151 · Cited by 20

Comparison of Grid-Based and Segment-Based Estimation of Forest Attributes Using Airborne Laser Scanning and Digital Aerial Imagery

Tuominen, S., & Haapanen, R. (2011) · Remote Sensing · doi:10.3390/rs3050945 · Cited by 20

Cluster-based data modeling for semantic video search

Tešić, J., Natsev, A., & Smith, J. R. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282365 · Cited by 20

Adaptive Subspace Symbolization for Content-Based Video Detection

Zhou, X., Zhou, X., Chen, L., Shu, Y., Bouguettaya, A., & Taylor, J. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.171 · Cited by 20

Single trial behavioral task classification using subthalamic nucleus local field potential signals

Niketeghad, S., Hebb, A. O., Nedrud, J., Hanrahan, S. J., & Mahoor, M. H. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944449 · Cited by 20

Learning Using Concave and Convex Kernels: Applications in Predicting Quality of Sleep and Level of Fatigue in Fibromyalgia

Sabeti, E., Gryak, J., Derksen, H., Biwer, C., Ansari, S., Isenstein, H., Kratz, A. L., & Najarian, K. (2019) · Entropy · doi:10.3390/e21050442 · Cited by 20

Accelerated Approximate Nearest Neighbors Search Through Hierarchical Product Quantization

Abdelhadi, A., Bouganis, C.-S., & Constantinides, G. A. (2019) · International Conference on Field-Programmable Technology (FPT) · doi:10.1109/icfpt47387.2019.00019 · Cited by 19

Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation

Chew, R., Kery, C., Baum, L., Bukowski, T., Kim, A. E., & Navarro, M. A. (2021) · JMIR Public Health and Surveillance · doi:10.2196/25807 · Cited by 22

Fuzzy support vector machine with graph for classifying imbalanced datasets

Chen, B., Fan, Y., Lan, W., Liu, J., Cao, C., & Gao, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.139 · Cited by 19

Immune signatures of common exposures through co-occurrence of T-cell receptors in tens of thousands of donors

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

Multi-objective optimization of shared nearest neighbor similarity for feature selection

Kundu, P. P., & Mitra, S. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.08.042 · Cited by 19

Projective Clustering Ensembles

Gullo, F., Domeniconi, C., & Tagarelli, A. (2009) · Proceedings · doi:10.1109/icdm.2009.131 · Cited by 19

A framework for data transformation in Credit Behavioral Scoring applications based on Model Driven Development

Neto, R. F. D. O., Adeodato, P. J. L., & Salgado, A. C. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.10.059 · Cited by 19

Double indices-induced FCM clustering and its integration with fuzzy subspace clustering

Wang, J., Chung, F.-L., Wang, S., & Deng, Z. (2013) · Pattern Analysis and Applications · doi:10.1007/s10044-013-0341-y · Cited by 19

Enabling Visual Action Planning for Object Manipulation Through Latent Space Roadmap

Lippi, M., Poklukar, P., Welle, M. C., Varava, A., Yin, H., Marino, A., & Kragić, D. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3188163 · Cited by 21

Searching with quantization: approximate nearest neighbor search using short codes and distance estimators

Jeǵou, H., Douze, M., & Schmid, C. (2009) · Cited by 19

Data Modeling Strategies for Imbalanced Learning in Visual Search

Tešić, J., Natsev, A., Xie, L., & Smith, J. R. (2007) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2007.4285069 · Cited by 19

Comparative Local Quality Assessment of 3D Medical Image Segmentations with Focus on Statistical Shape Model-Based Algorithms

von Landesberger, T., Basgier, D., & Becker, M. (2016) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2015.2501813 · Cited by 21

A sequential sampling strategy for adaptive classification of computationally expensive data

Singh, P., van der Herten, J., Deschrijver, D., Couckuyt, I., & Dhaene, T. (2016) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-016-1584-1 · Cited by 19

Novel technique for preprocessing high dimensional time-course data from DNA microarray: mathematical model-based clustering

Hakamada, K., Okamoto, M., & Hanai, T. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl016 · Cited by 19

Knowledge Discovery and Data Mining

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Dynamic asset allocation with asset-specific regime forecasts

Shu, Y., Yu, C., & Mulvey, J. M. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-06266-0 · Cited by 18

Prediction of groundwater quality index in the Gaza coastal aquifer using supervised machine learning techniques

Aish, A., Zaqoot, H. A., Sethar, W. A., & Aish, D. A. (2023) · Water Practice & Technology · doi:10.2166/wpt.2023.028 · Cited by 18

Residual Vector Product Quantization for approximate nearest neighbor search

Niu, L., Xu, Z. J., Zhao, L., He, D., Ji, J., Yuan, X., & Xue, M.-A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120832 · Cited by 18

Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance

Wild, R., Wodaczek, F., Tatto, V. D., Cheng, B., & Laio, A. (2025) · Nature Communications · doi:10.1038/s41467-024-55449-7 · Cited by 25

A Filter-And-Refine Indexing Method For Fast Similarity Search In Millions Of Music Tracks.

Schnitzer, D., Flexer, A., & Widmer, G. (2009) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1417830 · Cited by 18

Binarising SIFT-Descriptors to Reduce the Curse of Dimensionality in Histogram-Based Object Recognition

Stommel, M., & Herzog, O. (2009) · Communications in computer and information science · doi:10.1007/978-3-642-10546-3_38 · Cited by 18

On the Use of LSH for Privacy Preserving Personalization

Aghasaryan, A., Bouzid, M., Kostadinov, D. D., Kothari, M., & Nandi, A. (2013) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom.2013.46 · Cited by 18

EDISKCO

Hassani, M., Müller, E., & Seidl, T. (2009) · International Workshop on Knowledge Discovery from Sensor Data (SensorKDD) · doi:10.1145/1601966.1601975 · Cited by 18

Centroid-Based Actionable 3D Subspace Clustering

Sim, K., Yap, G.-E., Hardoon, D. R., Gopalkrishnan, V., Cong, G., & Lukman, S. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.37 · Cited by 18

Subspace Clustering Using Evolvable Genome Structure

Peignier, S., Rigotti, C., & Beslon, G. (2015) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/2739480.2754709 · Cited by 18

Subspace clustering for indexing high dimensional data

Günnemann, S., Kremer, H., Lenhard, D., & Seidl, T. (2011) · International Conference on Extending Database Technology (EDBT) · doi:10.1145/1951365.1951395 · Cited by 18

Biomedical Ontology MeSH Improves Document Clustering Qualify on MEDLINE Articles: A Comparison Study

Yoo, I., & Hu, X. (2006) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2006.62 · Cited by 18

CRAFTER: A Tree-Ensemble Clustering Algorithm for Static Datasets with Mixed Attributes and High Dimensionality

Lin, S., Azarnoush, B., & Runger, G. C. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2807444 · Cited by 18

Particle Swarm Optimisation for Feature Selection and Weighting in High-Dimensional Clustering

O’Neill, D., Lensen, A., Xue, B., & Zhang, M. (2018) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2018.8477974 · Cited by 18

MERLIN++: parameter-free discovery of time series anomalies

Nakamura, T., Mercer, R., Imamura, M., & Keogh, E. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00876-7 · Cited by 18

Constrained subspace classifier for high dimensional datasets

Panagopoulos, O. P., Pappu, V., Xanthopoulos, P., & Pardalos, P. M. (2015) · Omega · doi:10.1016/j.omega.2015.05.009 · Cited by 18

Learning and reuse of engineering ramp-up strategies for modular assembly systems

Scrimieri, D., Oates, R., & Ratchev, S. M. (2013) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-013-0839-6 · Cited by 18

Fast vector quantization for efficient rendering of compressed point-clouds

Schnabel, R., Möser, S., & Klein, R. (2008) · Computers & Graphics · doi:10.1016/j.cag.2008.01.014 · Cited by 18

Efficient Index-Based Approaches for Skyline Queries in Location-Based Applications

Lee, K. C. K., Zheng, B., Chen, C., & Chow, C.-Y. (2012) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.216 · Cited by 18

High-dimensional $p$-normsPreprint

Biau, G., & Mason, D. M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1311.0587 · Cited by 18

Robust correlation estimation and UMAP assisted topological analysis of omics data for disease subtyping

Rather, A. A., & Chachoo, M. A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106640 · Cited by 17

Applicability Domain Characterization for Machine Learning QSAR Models

Wang, Z., & Chen, J. (2023) · Computational methods in engineering & the sciences · doi:10.1007/978-3-031-20730-3_13 · Cited by 17

Efficient Methods in Finding Aggregate Nearest Neighbor by Projection-Based Filtering

Luo, Y., Chen, H., Furuse, K., & Ohbo, N. (2007) · Springer eBooks · doi:10.1007/978-3-540-74484-9_70 · Cited by 17

Metacluster-based Projective Clustering Ensembles

Gullo, F., Domeniconi, C., & Tagarelli, A. (2013) · Machine Learning · doi:10.1007/s10994-013-5395-y · Cited by 17

Efficient Nearest Neighbors via Robust Sparse Hashing

Cherian, A., Sra, S., Morellas, V., & Papanikolopoulos, N. P. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2324280 · Cited by 17

Investigating the Effectiveness of Laplacian-Based Kernels in Hub Reduction

Suzuki, I., Hara, K., Shimbo, M., Matsumoto, Y., & Saerens, M. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v26i1.8295 · Cited by 17

Nearest Neighbors Using Compact Sparse Codes

Cherian, A., & Hal, H. I. (2014) · Cited by 17

SNCStream+: Extending a high quality true anytime data stream clustering algorithm

Barddal, J. P., Gomes, H. M., Enembreck, F., & BARTHES, J.-P. A. (2016) · Information Systems · doi:10.1016/j.is.2016.06.007 · Cited by 17

Video Semantic Clustering with Sparse and Incomplete Tags

Wang, J., Zhu, X., & Gong, S. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10454 · Cited by 17

Measuring Concentration of Distances—An Effective and Efficient Empirical Index

Kumari, S., & Jayaram, B. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2622270 · Cited by 17

Groupwise structural parcellation of the whole cortex: A logistic random effects model based approach

Gallardo, G., Wells, W. M., Rachid Deriche, B., & Wassermann, D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.070 · Cited by 17

Content-based image retrieval based on multiple extended fuzzy-rough framework

Islam, S. M., Banerjee, M., Bhattacharyya, S., & Chakraborty, S. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.03.036 · Cited by 17

Graph regularized nonnegative sparse coding using incoherent dictionary for approximate nearest neighbor search

Zhang, Y., Xiang, M., & Yang, B. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.04.030 · Cited by 17

Scene Understanding For Real Time Processing Of Queries Over Big Data Streaming Video

Aved, A. J. (2013) · Journal of International Crisis and Risk Communication Research · Cited by 17

A Novel Technique of Using Coupled Matrix and Greedy Coordinate Descent for Multi-view Data Representation

Luong, K., Balasubramaniam, T., & Nayak, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-02925-8_20 · Cited by 17

Data-driven cluster analysis of insomnia disorder with physiology-based qEEG variables

McCloskey, S., Jeffries, B., Koprinska, I., Miller, C. B., & Grunstein, R. R. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.07.034 · Cited by 17

Curse of Dimensionality in Adversarial Examples

Chattopadhyay, N., Chattopadhyay, A., Gupta, S. S., & Kasper, M. E. (2019) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2019.8851795 · Cited by 20

High-dimensional data clustering by using local affine/convex hulls

Çevıkalp, H. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.10.007 · Cited by 17

An Interactive Visualization for Feature Localization in Deep Neural Networks

Zurowietz, M., & Nattkemper, T. W. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00049 · Cited by 18

Point-to-Hyperplane Nearest Neighbor Search Beyond the Unit Hypersphere

Huang, Q., Lei, Y., & Tung, A. K. H. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3457240 · Cited by 17

When is Nearest Neighbor Meaningful

Hui, A., & Gao, B. J. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3482219 · Cited by 17

NeuMapper: A scalable computational framework for multiscale exploration of the brain’s dynamical organization

Geniesse, C., Chowdhury, S., & Saggar, M. (2022) · Network Neuroscience · doi:10.1162/netn_a_00229 · Cited by 17

Investigation of strength behavior of thermally deteriorated sedimentary rocks subjected to dynamic cyclic loading

Waqas, U., & Ahmed, M. F. (2022) · International Journal of Rock Mechanics and Mining Sciences · doi:10.1016/j.ijrmms.2022.105201 · Cited by 17

Is the Distance Compression Effect Overstated? Some Theory and Experimentation

France, S. L., & Carroll, D. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03070-3_21 · Cited by 17

Data Analysis Challenges in the Future Energy Domain

Eichinger, F., Pathmaperuma, D., Vogt, H., & Müller, E. (2013) · Repository KITopen (Karlsruhe Institute of Technology) · doi:10.5445/ir/1000034312 · Cited by 17

Understanding Similarity Metrics in Neighbour-based Recommender Systems

Bellogín, A., & de Vries, A. P. (2013) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/2499178.2499186 · Cited by 17

Parsimonious temporal aggregation

Gordevičius, J., Gamper, J., & Böhlen, M. H. (2011) · The VLDB Journal · doi:10.1007/s00778-011-0243-9 · Cited by 17

When Is Nearest Neighbors Indexable?

Shaft, U., & Ramakrishnan, R. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30570-5_11 · Cited by 22

Theory of nearest neighbors indexability

Shaft, U., & Ramakrishnan, R. (2006) · ACM Transactions on Database Systems · doi:10.1145/1166074.1166077 · Cited by 52

Study on dimensionality curse in the nearest neighbor queries based on statistics

Bo, S., Li, S., & Chong-guang, Z. (2006) · Jisuanji gongcheng

Two algorithms for nearest-neighbor search in high dimensions

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