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

Agrawal, R., Faloutsos, C., & Swami, A. (1993) · Lecture notes in computer science · doi:10.1007/3-540-57301-1_5 · Cited by 1,989

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

Bellman, R. (1964) · IEEE Transactions on Circuit Theory · doi:10.1109/tct.1964.1082221 · Cited by 1,003

Embeddings-Based Clustering for Target Specific Stances: The Case of a Polarized Turkey

Rashed, A. A.-A., Kutlu, M., Darwish, K. M., Elsayed, T., & Bayrak, C. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v15i1.18082 · Cited by 49

A Comparative Study for Domain Ontology Guided Feature Extraction

Wang, B. B., McKay, B., Abbass, H. A., & Barlow, M. (2003) · Cited by 49

Reverse Nearest Neighbors Search in Ad Hoc Subspaces

Yiu, M. L., & Mamoulis, N. (2007) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.47 · Cited by 49

Extremely Fast and Accurate Open Modification Spectral Library Searching of High-Resolution Mass Spectra Using Feature Hashing and Graphics Processing Units

Bittremieux, W., Laukens, K., & Noble, W. S. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00291 · Cited by 49

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

Makarov, V. A., Lobov, S. A., Shchanikov, S., Mikhaylov, A. N., & Kazantsev, V. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.859874 · Cited by 48

Example-Based Robust Outlier Detection in High Dimensional Datasets

Zhu, C., Kitagawa, H., & Faloutsos, C. (2006) · Proceedings · doi:10.1109/icdm.2005.59 · Cited by 48

Autonomous navigation for reconfigurable snake-like robots in challenging, unknown environments

Pfotzer, L., Klemm, S., Roennau, A., Zöllner, J. M., & Dillmann, R. (2016) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2016.11.010 · Cited by 48

High-Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality

Gorban, A. N., Makarov, V. A., & Tyukin, I. (2020) · Entropy · doi:10.3390/e22010082 · Cited by 54

Real-time fire detection system based on dynamic time warping of multichannel sensor networks

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Fractional Distance Measures for Content-Based Image Retrieval

Howarth, P. H., & Rüger, S. (2005) · Lecture notes in computer science · doi:10.1007/978-3-540-31865-1_32 · Cited by 48

How to improve the pruning ability of dynamic metric access methods

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Semantic Discriminative Metric Learning for Image Similarity Measurement

Wang, H., Feng, L., Zhang, J., & Liu, Y. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2569412 · Cited by 47

Assisted Descriptor Selection Based on Visual Comparative Data Analysis

Bremm, S., von Landesberger, T., Bernard, J., & Schreck, T. (2011) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2011.01938.x · Cited by 47

Instream and offstream environmental conditions and stream biotic integrity

Bedoya, D., Novotný, V., & Manolakos, E. S. (2009) · Ecological Modelling · doi:10.1016/j.ecolmodel.2009.06.017 · Cited by 47

A Graphics Hardware Accelerated Algorithm for Nearest Neighbor Search

Bustos, B., Deußen, O., Hiller, S., & Keim, D. A. (2006) · Lecture notes in computer science · doi:10.1007/11758549_30 · Cited by 47

Maximal Subspace Coregulated Gene Clustering

Zhao, Y., Yu, J. X., Wang, G., Chen, L., Wang, B., & Yu, G. (2007) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.190670 · Cited by 47

Study on the Improvement of K-Nearest-Neighbor Algorithm

Sun, B., Du, J., & Gao, T. (2009) · doi:10.1109/aici.2009.312 · Cited by 47

CNN Feature Boosted SeqSLAM for Real‐Time Loop Closure Detection

Bai, D., Wang, C., Zhang, B., Yi, X., & Yang, X. (2018) · Chinese Journal of Electronics · doi:10.1049/cje.2018.03.010 · Cited by 47

Global Correlation Clustering Based on the Hough Transform

Achtert, E., Böhm, C., David, J., Kröger, P., & Zimek, A. (2008) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.10012 · Cited by 47

Value and Relation Display for Interactive Exploration of High Dimensional Datasets

Yang, J., Patro, A., Huang, S., Mehta, N., Ward, M. O., & Rundensteiner, E. A. (2004) · doi:10.1109/infovis.2004.71 · Cited by 47

On the Difficulty of Nearest Neighbor SearchPreprint

He, J., Kumar, S., & Chang, S. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.6411 · Cited by 47

Voting Ensemble Approach for Enhancing Alzheimer’s Disease Classification

Chatterjee, S., & Byun, Y.-C. (2022) · Sensors · doi:10.3390/s22197661 · Cited by 49

Mining Motifs from Human Motion

Meng, J., Yuan, J., Hans, M., & Wu, Y. N. (2008) · doi:10.2312/egs.20081024 · Cited by 46

Brain meta-state transitions demarcate thoughts across task contexts exposing the mental noise of trait neuroticism

Tseng, J., & Poppenk, J. (2020) · Nature Communications · doi:10.1038/s41467-020-17255-9 · Cited by 50

New trends in high-D vector similarity search

Echihabi, K., Zoumpatianos, K., & Palpanas, T. (2021) · Proceedings of the VLDB Endowment · doi:10.14778/3476311.3476407 · Cited by 46

An axiomatic approach to intrinsic dimension of a dataset

Pestov, V. (2008) · Neural Networks · doi:10.1016/j.neunet.2007.12.030 · Cited by 45

Multimedia search and retrieval using multimodal annotation propagation and indexing techniques

Lazaridis, M., Αξενόπουλος, Α., Rafailidis, D., & Daras, P. (2012) · Signal Processing Image Communication · doi:10.1016/j.image.2012.04.001 · Cited by 45

Listener-Speaker Perceived Distance Predicts the Degree of Motor Contribution to Speech Perception

Bartoli, E., D’Ausilio, A., Berry, J., Badino, L., Bever, T. G., & Fadiga, L. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht257 · Cited by 49

Spatial Transcriptomics: Molecular Maps of the Mammalian Brain

Ortiz, C., Carlén, M., & Meletis, K. (2021) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-100520-082639 · Cited by 48

LDC: enabling search by partial distance in a hyper-dimensional space

Koudas, N., Ooi, B. C., Shen, H. T., & Tung, A. K. H. (2004) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2004.1319980 · Cited by 45

Projected Residual Vector Quantization for ANN Search

Wei, B., Guan, T., & Yu, J. (2014) · IEEE Multimedia · doi:10.1109/mmul.2013.65 · Cited by 44

Manifold-ranking based video concept detection on large database and feature pool

Yuan, X., Hua, X., Wang, M., & Wu, X. (2006) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1180639.1180768 · Cited by 44

An Improved Co-Similarity Measure for Document Clustering

Hussain, S. F., Bisson, G., & Grimal, C. (2010) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2010.35 · Cited by 44

Dimensionality Reduction for Spectral Clustering

Niu, D., Dy, J., & Jordan, M. I. (2011) · Cited by 44

Automatic Comparison of Load Tests to Support the Performance Analysis of Large Enterprise Systems

Malik, H., Jiang, Z. M., Adams, B., Hassan, A. E., Flora, P., & Hamann, G. F. (2010) · Euromicro Conference on Software Maintenance and Reengineering (CSMR) · doi:10.1109/csmr.2010.39 · Cited by 44

Spatially Constrained Fuzzy-Clustering-Based Sensor Placement for Spatiotemporal Fuzzy-Control System

Zhang, X., Li, H., & Qi, C. (2010) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2010.2058810 · Cited by 44

Why Clusters and Other Patterns Can Seem to be Found in Analyses of High-Dimensional Data

Rohlf, F. J. (2020) · Evolutionary Biology · doi:10.1007/s11692-020-09518-6 · Cited by 44

Foundations and Advances in Data Mining

Chu, W., & Lin, T. Y. (2005) · Studies in fuzziness and soft computing · doi:10.1007/b104039 · Cited by 44

Quantitative Analysis of Nearest-Neighbors Search in High-Dimensional Sampling-Based Motion Planning

Plaku, E., & Kavraki, L. E. (2008) · Springer tracts in advanced robotics · doi:10.1007/978-3-540-68405-3_1 · Cited by 44

Indexing Metric Spaces for Exact Similarity Search

Chen, L., Gao, Y., Song, X., Li, Z., Zhu, Y., Miao, X., & Jensen, C. S. (2022) · ACM Computing Surveys · doi:10.1145/3534963 · Cited by 44

Optimizing Euclidean Distance Computation

Mussabayev, R. (2024) · Mathematics · doi:10.3390/math12233787 · Cited by 45

Fast and Accurate Hashing Via Iterative Nearest Neighbors Expansion

Jin, Z., Zhang, D., Hu, Y., Lin, S., Cai, D., & He, X. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2302018 · Cited by 43

Enhancing principal direction divisive clustering

Tasoulis, S. K., Tasoulis, D. K., & Plagianakos, V. P. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.05.025 · Cited by 43

EEG signal classification based on SVM with improved squirrel search algorithm

Shi, M., Wang, C., Li, X., Li, M.-Q., Wang, L., & Xie, N. (2020) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2020-0038 · Cited by 44

Fast and reliable analysis of molecular motion using proximity relations and dimensionality reduction

Plaku, E., Stamati, H., Clementi, C., & Kavraki, L. E. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21337 · Cited by 43

Workers’ Trust in Collaborative Construction Robots: EEG-Based Trust Recognition in an Immersive Environment

Shayesteh, S., Ojha, A., & Jebelli, H. (2022) · Springer eBooks · doi:10.1007/978-3-030-77163-8_10 · Cited by 42

Noise Level Estimation for Model Selection in Kernel PCA Denoising

Varon, C., Alzate, C., & Suykens, J. A. K. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2388696 · Cited by 42

A fast MST-inspired kNN-based outlier detection method

Wang, X., Wang, X., Ma, Y., & Wilkes, D. M. (2014) · Information Systems · doi:10.1016/j.is.2014.09.002 · Cited by 42

Similarity search: a matching based approach

Tung, A. K. H., Zhang, R., Koudas, N., & Ooi, B. C. (2006) · doi:10.5555/1182635.1164182 · Cited by 42

Towards meaningful high-dimensional nearest neighbor search by human-computer interaction

Aggarwal, C. C. (2003) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2002.994777 · Cited by 42

Ranking Spatial Data by Quality Preferences

Yiu, M. L., Lu, H. C., Mamoulis, N., & Vaitis, M. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.119 · Cited by 42

Predicting and tracking internet path changes

Cunha, Í. F. S., Teixeira, R., Veitch, D. N., & Diot, C. (2011) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/2018436.2018451 · Cited by 42

Predicting cancer cell invasion by single-cell physical phenotyping

Nyberg, K. D., Bruce, S. L., Nguyen, A. V., Chan, C., Gill, N. K., Kim, T.-H., Sloan, E. K., & Rowat, A. C. (2018) · Integrative Biology · doi:10.1039/c7ib00222j · Cited by 43

Choosing the proper autoencoder for feature fusion based on data complexity and classifiers: Analysis, tips and guidelines

Pulgar, F. J., Charte, F., Rivera, A. J., & del Jesús, M. J. (2019) · Information Fusion · doi:10.1016/j.inffus.2019.07.004 · Cited by 42

Railway Infrastructure Classification and Instability Identification Using Sentinel-1 SAR and Laser Scanning Data

Chang, L., Sakpal, N. P., Elberink, S. O., & Wang, H. (2020) · Sensors · doi:10.3390/s20247108 · Cited by 42

A data-driven missing value imputation approach for longitudinal datasets

Ribeiro, C., & Freitas, A. A. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09963-5 · Cited by 42

Mining Statistical Association Rules to Select the Most Relevant Medical Image Features

Ribeiro, M. X., Balan, A. G. R., Felipe, J. C., Traina, A. J. M., & Traina, C. (2008) · Studies in computational intelligence · doi:10.1007/978-3-540-88067-7_7 · Cited by 42

Machine learning based typology development in archaeology

Hörr, C., Lindinger, E., & Brunnett, G. (2014) · Journal on Computing and Cultural Heritage · doi:10.1145/2533988 · Cited by 45

CLUSTERING METHODS IN PROTEIN-PROTEIN INTERACTION NETWORK

Lin, C., Cho, Y., Hwang, W., Pei, P., & Zhang, A. (2006) · Cited by 41

The blind men and the elephant: on meeting the problem of multiple truths in data from clustering and pattern mining perspectives

Zimek, A., & Vreeken, J. (2013) · Machine Learning · doi:10.1007/s10994-013-5334-y · Cited by 41

Brute-Force k-Nearest Neighbors Search on the GPU

Li, S., & Amenta, N. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25087-8_25 · Cited by 41

Data Driven Chiller Sequencing for Reducing HVAC Electricity Consumption in Commercial Buildings

Zheng, Z., Chen, Q., Fan, C., Guan, N., Vishwanath, A., Wang, D., & Liu, F. (2018) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3208903.3208913 · Cited by 41

Secure multimodal biometric system based on diffused graphs and optimal score fusion

Walia, G. S., Rishi, S., Asthana, R. K., Kumar, A., & Gupta, A. R. (2019) · IET Biometrics · doi:10.1049/iet-bmt.2018.5018 · Cited by 41

Exploring multi-scale spatial patterns in historical avalanche data, Jackson Hole Mountain Resort, Wyoming

McCollister, C. M., Birkeland, K. W., Hansen, K. J., Aspinall, R., & Comey, R. H. (2003) · Cold Regions Science and Technology · doi:10.1016/s0165-232x(03)00072-7 · Cited by 43

iHelp: An Intelligent Online Helpdesk System

Wang, D., Li, T., Shenghuo Zhu, B., & Gong, Y. (2010) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2010.2049352 · Cited by 41

Global, spatially explicit modelling of zenith wet delay with XGBoost

Crocetti, L., Schartner, M., Zus, F., Zhang, W., Möller, G., Navarro, V., See, L., Konrad Schindler, B., & Soja, B. S. (2024) · Journal of Geodesy · doi:10.1007/s00190-024-01829-2 · Cited by 41

An adaptive and dynamic dimensionality reduction method for high-dimensional indexing

Shen, H. T., Zhou, X., & Zhou, A. (2005) · The VLDB Journal · doi:10.1007/s00778-005-0167-3 · Cited by 40

ATLAS

Zhai, J., Lou, Y., & Gehrke, J. E. (2011) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/1989323.1989428 · Cited by 40

Vector quantization: a review

Wu, Z., & Yu, J. (2019) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700833 · Cited by 40

Natural language processing and machine learning to enable automatic extraction and classification of patients’ smoking status from electronic medical records

Caccamisi, A., Jørgensen, L., Dalianis, H., & Rosenlund, M. (2020) · Upsala Journal of Medical Sciences · doi:10.1080/03009734.2020.1792010 · Cited by 40

EBOD: An ensemble-based outlier detection algorithm for noisy datasets

Ouyang, B., Song, Y. J., Li, Y., Sant, G., & Bauchy, M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107400 · Cited by 40

LearnLoc: A framework for smart indoor localization with embedded mobile devices

Pasricha, S., Ugave, V., Anderson, C. W., & Han, Q. (2015) · International Conference on Hardware/Software Codesign and System Synthesis · doi:10.1109/codesisss.2015.7331366 · Cited by 40

High-performance data mining with skeleton-based structured parallel programming

Coppola, M., & Vanneschi, M. (2002) · Parallel Computing · doi:10.1016/s0167-8191(02)00095-9 · Cited by 40

Personalized Hierarchical Clustering

Bade, K., & Nürnberger, A. (2006) · IEEE/WIC/ACM International Conference on Web Intelligence · doi:10.1109/wi.2006.131 · Cited by 40

Using space-time grid for efficient management of moving objects

Chon, H. D., Agrawal, D., & Abbadi, A. E. (2001) · ACM International Workshop on Data Engineering for Wireless and Mobile Access (MobiDE) · doi:10.1145/376868.376899 · Cited by 40

Towards effective and interpretable data mining by visual interaction

Aggarwal, C. C. (2002) · ACM SIGKDD Explorations Newsletter · doi:10.1145/507515.507518 · Cited by 40

A machine learning model for multi-class classification of quenched and partitioned steel microstructure type by the k-nearest neighbor algorithm

Gupta, A. K., Chakroborty, S., Ghosh, S. K., & Ganguly, S. (2023) · Computational Materials Science · doi:10.1016/j.commatsci.2023.112321 · Cited by 39

HmmSeNet: A Novel Single Domain Generalization Equipment Fault Diagnosis Under Unknown Working Speed Using Histogram Matching Mixup

Tang, J. X., Ding, X., Wei, C., Xiao, J., Liu, R., Wang, L., & Huang, W. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3353922 · Cited by 39

A novel algorithm for fast and scalable subspace clustering of high-dimensional data

Kaur, A., & Datta, A. (2015) · Journal Of Big Data · doi:10.1186/s40537-015-0027-y · Cited by 39

A theoretical analysis of Deep Neural Networks for texture classification

Basu, S., Karki, M., Mukhopadhyay, S., Ganguly, S., Nemani, R., DiBiano, R., & Gayaka, S. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727306 · Cited by 39

A clustering algorithm for stream data with LDA-based unsupervised localized dimension reduction

Laohakiat, S., Phimoltares, S., & Lursinsap, C. (2016) · Information Sciences · doi:10.1016/j.ins.2016.11.018 · Cited by 39

Status quo and opportunities for building energy prediction in limited data Context—Overview from a competition

Xiao, T., Xu, P., He, R., & Sha, H. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117829 · Cited by 39

Clustering in Massive Data Sets

Murtagh, F. D. (2002) · Massive computing · doi:10.1007/978-1-4615-0005-6_14 · Cited by 39

Local Dimensionality Reduction for Non-Parametric Regression

Hoffmann, H., Schaal, S., & Vijayakumar, S. (2009) · Neural Processing Letters · doi:10.1007/s11063-009-9098-0 · Cited by 40

Finding near neighbors through cluster pruning

Chierichetti, F., Panconesi, A., Raghavan, P., Sozio, M., Tiberi, A., & Upfal, E. (2007) · ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/1265530.1265545 · Cited by 39

Efficient region-based image retrieval

Weber, R., & Mlivoncic, M. (2003) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/956863.956878 · Cited by 39

Machine Learning-Based Water Quality Classification Assessment

Chen, W., Xu, D., Pan, B., Zhao, Y. C., & Song, Y. X. (2024) · Water · doi:10.3390/w16202951 · Cited by 38

A sign that spells

Offert, F., & Phan, T. (2024) · Journal of Digital Social Research · doi:10.33621/jdsr.v6i440462 · Cited by 38

A novel two-stage feature selection method based on random forest and improved genetic algorithm for enhancing classification in machine learning

Ding, J., Du, J., Wang, H., & Xiao, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-01761-1 · Cited by 38

A general grid-clustering approach

Yue, S., Wei, M., Wang, J.-S., & Wang, H. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.02.019 · Cited by 38

Text document clustering using global term context vectors

Kalogeratos, A., & Likas, A. C. (2011) · Knowledge and Information Systems · doi:10.1007/s10115-011-0412-6 · Cited by 38

Graph-Based Label Propagation in Digital Media

Zoidi, O., Fotiadou, E., Nikolaidis, N., & Pitas, I. (2015) · ACM Computing Surveys · doi:10.1145/2700381 · Cited by 38

Detection of orthogonal concepts in subspaces of high dimensional data

Günnemann, S., Müller, E., Färber, I., & Seidl, T. (2009) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/1645953.1646120 · Cited by 38

Identifying On-Site Users for Social Events: Mobility, Content, and Social Relationship

Yu, Z., Yi, F., Lv, Q., & Guo, B. (2018) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2018.2794981 · Cited by 38

Product quantization with dual codebooks for approximate nearest neighbor search

Pan, Z., Wang, L., Wang, Y., & Liu, Y. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.03.016 · Cited by 38

El Niño Detection Via Unsupervised Clustering of Argo Temperature Profiles

Houghton, I., & Wilson, J. D. (2020) · Journal of Geophysical Research Oceans · doi:10.1029/2019jc015947 · Cited by 38

Distinctiveness-sensitive nearest-neighbor search for efficient similarity retrieval of multimedia information

Katayama, N., & Satoh, S. (2002) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2001.914863 · Cited by 38

Pivot selection for metric-space indexing

Mao, R., Zhang, P., Li, X., Liu, X., & Lu, M. (2016) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-016-0504-4 · Cited by 37

Visualizing probabilistic models and data with Intensive Principal Component Analysis

Quinn, K. N., Clement, C. B., Bernardis, F. D., Niemack, M. D., & Sethna, J. P. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1817218116 · Cited by 37

Sequence searching with CNN features for robust and fast visual place recognition

Bai, D., Wang, C., Zhang, B., Yi, X., & Yang, X. (2017) · Computers & Graphics · doi:10.1016/j.cag.2017.07.019 · Cited by 37

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

Kleinberg, J. M. (1997) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/258533.258653 · Cited by 347

Density-based indexing for approximate nearest-neighbor queries

Bennett, K. P., Fayyad, U. M., & Geiger, D. (1999) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312236 · Cited by 97