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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Finding Clusters in subspaces of very large, multi-dimensional datasets

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Remodularization analysis using semantic clustering

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Time series clustering with random convolutional kernels

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Projective clustering ensembles

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Effectiveness of NAQ-tree as index structure for similarity search in high-dimensional metric space

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EDSC

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A methodology to optimize query in wireless sensor networks using historical data

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Flexible and adaptive subspace search for outlier analysis

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Dimension Reduction of Optical Remote Sensing Images via Minimum Change Rate Deviation Method

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A SVM-kNN method for quasar-star classification

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Non-Invasive Silent Phoneme Recognition Using Microwave Signals

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Comparing high-dimensional confounder control methods for rapid cohort studies from electronic health records

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Similarity search in sensor networks using semantic-based caching

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An interpretable machine learning-based model for shear resistance prediction of CFRP-strengthened RC beams using experimental and synthetic dataset

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Combining instance selection methods based on data characterization: An approach to increase their effectiveness

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Projected clustering for categorical datasets

Kim, M., & Ramakrishna, R. S. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2006.01.011 · Cited by 26

Gestalt: fast, unified fault localization for networked systems

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Generating data series query workloads

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Reusing Ambient Light to Recognize Hand Gestures

Kaholokula, M.-D. A. (2016) · Cited by 26

The role of local dimensionality measures in benchmarking nearest neighbor search

Aumüller, M., & Ceccarello, M. (2021) · Information Systems · doi:10.1016/j.is.2021.101807 · Cited by 26

Medical image retrieval via nearest neighbor search on pre-trained image features

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Problems of Fuzzy c-Means Clustering and Similar Algorithms with High Dimensional Data Sets

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Recursive‐clustering‐based approach for denial of service (DoS) attacks in wireless sensors networks

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A fast audio similarity retrieval method for millions of music tracks

Schnitzer, D., Flexer, A., & Widmer, G. (2010) · Multimedia Tools and Applications · doi:10.1007/s11042-010-0679-8 · Cited by 26

Scalable density-based subspace clustering

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DTRACK: A System to Predict and Track Internet Path Changes

Cunha, Í. F. S., Teixeira, R., Veitch, D. N., & Diot, C. (2013) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2013.2269837 · Cited by 26

Analysing and characterising optimization problems using length scale

Morgan, R., & Gallagher, M. R. (2015) · Soft Computing · doi:10.1007/s00500-015-1878-z · Cited by 26

Distance phenomena in high‐dimensional chemical descriptor spaces: Consequences for similarity‐based approaches

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Cluster-Driven Navigation of the Query Space

Sellam, T., & Kersten, M. L. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2515590 · Cited by 26

Text clustering based on good aggregations

Andreas Hotho, A., Maedche, A., & Staab, S. (2002) · Proceedings · doi:10.1109/icdm.2001.989577 · Cited by 26

Curse of dimensionality in pivot-based indexesPreprint

Volnyansky, I., & Pestov, V. (2009) · Cited by 26

Perceived Biological and Social Characteristics of a Representative Set of German First Names

Nett, T., Dorrough, A. R., Jekel, M., & Glöckner, A. (2019) · Social Psychology · doi:10.1027/1864-9335/a000383 · Cited by 25

A Line Complex-Based Evolutionary Algorithm for Many-Objective Optimization

Zhang, L., Kang, Q., Deng, Q., Xu, L., & Wu, Q. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123495 · Cited by 25

An adaptive highly improving the accuracy of clustering algorithm based on kernel density estimation

Pu, Y., Yao, W., Li, X., & Alhudhaif, A. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120187 · Cited by 25

Bayesian integration of genetics and epigenetics detects causal regulatory SNPs underlying expression variability

Das, A., Morley, M. P., Moravec, C. S., Tang, W. H. W., Hakonarson, H. H., Ashley, E. A., Brandimarto, J., Hu, R., Li, M., Li, H., et al. (2015) · Nature Communications · doi:10.1038/ncomms9555 · Cited by 25

The Hubness Phenomenon: Fact or Artifact?

Low, T., Borgelt, C., Stober, S., & Nürnberger, A. (2012) · Studies in fuzziness and soft computing · doi:10.1007/978-3-642-30278-7_21 · Cited by 25

Blackthorn: Large-Scale Interactive Multimodal Learning

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Accelerating EM clustering to find high-quality solutions

ORDÓÑEZ, C. R., & Omiecinski, E. R. (2004) · Knowledge and Information Systems · doi:10.1007/s10115-003-0141-6 · Cited by 25

Similarity caching

Chierichetti, F., Kumar, R., & Vassilvitskii, S. (2009) · ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/1559795.1559815 · Cited by 25

IFD: Iterative Feature and Data Clustering

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SEAL-II - The Soft Spot between Richly Structured and Unstructured Knowledge

Andreas Hotho, A., Maedche, A., Maedche, E., Staab, S., & Rudi Studer, A. (2001) · MADOC (University of Mannheim) · Cited by 25

knnAUC: an open-source R package for detecting nonlinear dependence between one continuous variable and one binary variable

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K Important Neighbors: A Novel Approach to Binary Classification in High Dimensional Data

Shahraki, H. R., Pourahmad, S., & Zare, N. (2017) · BioMed Research International · doi:10.1155/2017/7560807 · Cited by 26

Insight Beyond Numbers: The Impact of Qualitative Factors on Visual Data Analysis

Karer, B., Hagen, H., & Lehmann, D. J. (2020) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2020.3030376 · Cited by 24

A general methodology for n-dimensional trajectory clustering

Bermingham, L., & Lee, I. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.06.014 · Cited by 24

Visualnostics: Visual Guidance Pictograms for Analyzing Projections of High‐dimensional Data

Lehmann, D. J., Kemmler, F., Zhyhalava, T., Kirschke, M., & Theisel, H. (2015) · Computer Graphics Forum · doi:10.1111/cgf.12641 · Cited by 24

Bounded similarity querying for time-series data

Goldin, D., Millstein, T. D., & Kutlu, A. (2004) · Information and Computation · doi:10.1016/j.ic.2004.07.001 · Cited by 24

Towards large scale continuous EDA

Kabán, A., Bootkrajang, J., & Durrant, R. J. (2013) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/2463372.2463423 · Cited by 24

Shrink: An OLAP operation for balancing precision and size of pivot tables

Golfarelli, M., Graziani, S., & Rizzi, S. (2014) · Data & Knowledge Engineering · doi:10.1016/j.datak.2014.07.004 · Cited by 24

Querying process models by behavior inclusion

Kunze, M., Weidlich, M., & Weske, M. (2013) · Software & Systems Modeling · doi:10.1007/s10270-013-0389-6 · Cited by 24

Determining provenance in phishing websites using automated conceptual analysis

Layton, R., & Watters, P. (2009) · eCrime Researchers Summit (eCrime) · doi:10.1109/ecrime.2009.5342614 · Cited by 24

Data Combination and Feature Selection for Multi-source Forest Inventory

Haapanen, R., & Tuominen, S. (2008) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.74.7.869 · Cited by 24

Cross-view human action recognition from depth maps using spectral graph sequences

Kerola, T., Inoue, N., & Shinoda, K. (2016) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.10.004 · Cited by 24

On using machine learning to identify knowledge in API reference documentation

Fucci, D., Mollaalizadehbahnemiri, A., & Maalej, W. (2019) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3338906.3338943 · Cited by 24

Reducing distance computations for distance-based outliers

Angiulli, F., Basta, S., Lodi, S., & Sartori, C. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113215 · Cited by 24

Tagging and classifying facial images in cloud environments based on KNN using MapReduce

Kai, S., Kang, H., & Park, H. (2015) · Optik · doi:10.1016/j.ijleo.2015.07.080 · Cited by 24

Orthogonal moment-based descriptors for pose shape query on 3D point cloud patches

Cheng, H., & Chung, S. M. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.09.028 · Cited by 24

Hyperspectral image classification using spectral and spatial information based linear discriminant analysis

Li, C., Chu, H.-S., Kuo, B., & Lin, C. (2011) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2011.6049566 · Cited by 24

Maximum rank query

Mouratidis, K., Zhang, J., & Pang, H. (2015) · Proceedings of the VLDB Endowment · doi:10.14778/2824032.2824053 · Cited by 24

Implementing KDB-trees to support high-dimensional data

Orlandić, R., & Yu, B. (2002) · International Database Engineering and Applications Symposium (IDEAS) · doi:10.1109/ideas.2001.938071 · Cited by 24

Intrinsic dimension of a dataset: what properties does one expectPreprint

Pestov, V. (2007) · Cited by 24

Evaluation of apple juice quality using spectral fluorescence signatures

Poryvkina, L., Tsvetkova, N., & Sobolev, I. (2013) · Food Chemistry · doi:10.1016/j.foodchem.2013.11.131 · Cited by 23

Exploring Deep Space

Vuurens, J. B. P., Larson, M., & de Vries, A. P. (2016) · Workshop on Deep Learning for Recommender Systems (DLRS) · doi:10.1145/2988450.2988457 · Cited by 25

XAIRF-WFP: a novel XAI-based random forest classifier for advanced email spam detection

Bouke, M. A., Alramli, O. I., & Abdullah, A. (2024) · International Journal of Information Security · doi:10.1007/s10207-024-00920-1 · Cited by 23

Clustering by Pattern Similarity

Wang, H., & Pei, J. (2008) · Journal of Computer Science and Technology · doi:10.1007/s11390-008-9148-5 · Cited by 23

NOCEA: A rule-based evolutionary algorithm for efficient and effective clustering of massive high-dimensional databases

Sarafis, I., Trinder, P. W., & Zalzala, A. M. S. (2006) · Applied Soft Computing · doi:10.1016/j.asoc.2006.01.011 · Cited by 23

Data Mining Methods for Discovering Interesting Exceptions from an Unsupervised Table

Suzuki, E. (2006) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.3217/jucs-012-06-0627 · Cited by 23

Probabilistic Semi-Supervised Learning via Sparse Graph Structure Learning

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Clustering Categorical Data via Ensembling Dissimilarity Matrices

Amiri, S., Clarke, B. S., & Clarke, J. (2017) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2017.1305278 · Cited by 23

High-Dimensional Similarity Search for Scalable Data Science

Echihabi, K., Zoumpatianos, K., & Palpanas, T. (2021) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde51399.2021.00268 · Cited by 23

Visual Analytics and Interactive Technologies

Zhang, Q., Segall, R. S., & Cao, M. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-102-7 · Cited by 23

Large-Scale Image Mining with Flickr Groups

Ginsca, A., Popescu, A., Borgne, H. L., Ballas, N., Vo, P., & Kanellos, I. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-14445-0_28 · Cited by 23

Text Categorization Using Word Similarities Based on Higher Order Co-occurrences

Hussain, S. F., & Bisson, G. (2010) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972801.1 · Cited by 23

Efficient online top-K retrieval with arbitrary similarity measures

Deshpande, P., Deepak, P., & Kummamuru, K. (2008) · International Conference on Extending Database Technology (EDBT) · doi:10.1145/1353343.1353388 · Cited by 23

Non-Euclidean Norms and Data Normalisation

Doherty, K. A. J., Adams, R., & Davey, N. (2004) · University of Hertfordshire Research Archive (University of Hertfordshire) · Cited by 23

Algorithms for clustering high dimensional and distributed data

Li, T., Shenghuo Zhu, B., & Ogihara, M. (2003) · Intelligent Data Analysis · doi:10.3233/ida-2003-7404 · Cited by 23

A robust and efficient clustering algorithm based on cohesion self-merging

Lin, C.-R., & Chen⋆, M.-S. (2002) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/775047.775133 · Cited by 23

Redundant Bit Vectors for Quickly Searching High-Dimensional Regions

Goldstein, J., Plat, J. C., & Burges, C. J. C. (2005) · Lecture Notes in Computer Science · doi:10.1007/11559887_9 · Cited by 22

Randomized Locality Sensitive Vocabularies for Bag-of-Features Model

Mu, Y., Sun, J., Han, T. X., Cheong, L., & Yan, S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15558-1_54 · Cited by 22

Automatic feature extraction and selection for condition monitoring and related datasets

Schneider, T., Helwig, N., & Schütze, A. (2018) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2018.8409763 · Cited by 22

Learning Inter- and Intra-manifolds for Matrix Factorization-based Multi-Aspect Data Clustering

Luong, K., & Nayak, R. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3022072 · Cited by 22

A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning

Wang, H., Wang, X., Han, J., Xiang, H. Y., Li, H., Zhang, Y., & Li, S. (2022) · Sensors · doi:10.3390/s22020644 · Cited by 24

Machine learning for soft and liquid molecular materials

Orlova, T., Piven, A. O., Darmoroz, D. D., Aliev, T. A., Razik, T. M. T. A., Boitsev, A. A., Grafeeva, N., & Скорб, Е. В. (2023) · Digital Discovery · doi:10.1039/d2dd00132b · Cited by 23

An Efficient Sequential Embedding ConvNet for Rotating Machinery Intelligent Fault Diagnosis

Tang, J. X., Wú, Q., Li, X., Wei, C., Ding, X., Huang, W., & Shao, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3267376 · Cited by 22

Automatic pediatric otitis detection by classification of global image features

Mironică, I., Vertan, C., & Gheorghe, D. C. (2011) · E-Health and Bioengineering Conference (EHB) · Cited by 22

MiMAG: mining coherent subgraphs in multi-layer graphs with edge labels

Boden, B., Günnemann, S., Hoffmann, H., & Seidl, T. (2016) · Knowledge and Information Systems · doi:10.1007/s10115-016-0949-5 · Cited by 22

RON-Gauss: Enhancing Utility in Non-Interactive Private Data Release

Chanyaswad, T., Liu, C., & Mittal, P. (2018) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2019-0003 · Cited by 27

Stacked Regression and Poststratification

Ornstein, J. T. (2019) · Political Analysis · doi:10.1017/pan.2019.43 · Cited by 22

Probability Distributions for Analog-To-Target Distances

Platzer, P., Yiou, P., Naveau, P., Filipot, J., Thiébaut, M., & Tandeo, P. (2021) · Journal of the Atmospheric Sciences · doi:10.1175/jas-d-20-0382.1 · Cited by 22

Effective shape-based retrieval and classification of mammograms

Felipe, J. C., Ribeiro, M. X., de Sousa, E. P. M., Traina, A. J. M., & Traina, C. (2006) · ACM Symposium on Applied Computing · doi:10.1145/1141277.1141333 · Cited by 22

Noise fuzzy clustering of time series by autoregressive metric

D’Urso, P., Giovanni, L. D., Massari, R., & Lallo, D. D. (2013) · METRON · doi:10.1007/s40300-013-0024-x · Cited by 22

ELMVIS+: Fast nonlinear visualization technique based on cosine distance and extreme learning machines

Akusok, A., Baek, S., Miché, Y., Björk, K.-M., Nian, R., Lauren, P., & Lendasse, A. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.04.039 · Cited by 22

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

Indexing schemes for similarity search in datasets of short protein fragments

Stojmirović, A., & Pestov, V. (2007) · Information Systems · doi:10.1016/j.is.2007.03.001 · Cited by 22

The Elements of Visual Art Recommendation

Yilma, B. A., & Leiva, L. A. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581477 · Cited by 25

Wave-Function Network Description and Kolmogorov Complexity of Quantum Many-Body Systems

Mendes-Santos, T., Schmitt, M., Angelone, A., Rodríguez, Á., Scholl, P., Williams, H. J., Barredo, D., Lahaye, T., Browaeys, A., Heyl, M., et al. (2024) · Physical Review X · doi:10.1103/physrevx.14.021029 · Cited by 27

Filaments of Meaning in Word Space

Jussi Karlgren, C., Holst, A., & Sahlgren, M. (2008) · Cited by 21

Projective ART with buffers for the high dimensional space clustering and an application to discover stock associations

Liu, L., Huang, L., Lai, M., & Ma, C. (2008) · Neurocomputing · doi:10.1016/j.neucom.2008.01.020 · Cited by 21

Estimation of forest biomass by means of genetic algorithm-based optimization of airborne laser scanning and digital aerial photograph features

Tuominen, S., & Haapanen, R. (2013) · Silva Fennica · doi:10.14214/sf.902 · Cited by 21

Authenticated Multistep Nearest Neighbor Search

Papadopoulos, S., Wang, L., Yang, Y., Dimitris Papadias, C., & Karras, P. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.157 · Cited by 21

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