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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Improving semi-supervised co-forest algorithm in evolving data streams

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On quantum ensembles of quantum classifiers

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Analysis of the Factors Affecting Student Performance Using a Neuro-Fuzzy Approach

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Visualizing asymmetric proximities with SOM and MDS models

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Unsupervised learning algorithms for intrusion detection

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Structure in Complex Networks

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Investigating the difference between trolls, social bots, and humans on Twitter

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Document clustering using synthetic cluster prototypes

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A graph-based semi-supervised k nearest-neighbor method for nonlinear manifold distributed data classification

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Semi-supervised learning with sparse grids

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Heavy-tailed distributions and multi-keyword queries

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Formulating distance functions via the kernel trick

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High-dimensional index structures database support for next decade's applications (tutorial)

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A comparison of Gap statistic definitions with and without logarithm functionPreprint

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Projected outlier detection in high-dimensional mixed-attributes data set

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Spatial Skyline Queries: An Efficient Geometric Algorithm

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Clustering of time-series subsequences is meaningless

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Private Approximate Nearest Neighbor Search with Sublinear Communication

Servan-Schreiber, S., Langowski, S., & Devadas, S. (2022) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp46214.2022.9833702 · Cited by 39

Morpheus

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A framework for ontology-driven subspace clustering

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Autoregressive metric-based trimmed fuzzy clustering with an application to PM10 time series

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ClusterTree: Integration of cluster representation and nearest-neighbor search for large data sets with high dimensions

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Fast Evaluation Techniques for Complex Similarity Queries

Böhm, K., Mlivoncic, M., Schek, H., & Weber, R. (2001) · Cited by 35

Query Workloads for Data Series Indexes

Zoumpatianos, K., Lou, Y., Palpanas, T., & Gehrke, J. E. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783382 · Cited by 34

Mp-Dissimilarity: A Data Dependent Dissimilarity Measure

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Distance geometry and data science

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Estimating region-specific fuel economy in the United States from real-world driving cycles

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Disburdening the species conservation evolutionary algorithm of arguing with radii

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Recent advances in cluster analysis

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Efficient similarity search using the Earth Mover's Distance for large multimedia databases

Assent, I., Wichterich, M., Meisen, T., & Seidl, T. (2008) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2008.4497439 · Cited by 34

Clustering Via Decision Tree Construction

Liu, B., Xia, Y., & Yu, P. S. (2005) · Studies in fuzziness and soft computing · doi:10.1007/11362197_5 · Cited by 34

Clustering of sparse data via network communities—a prototype study of a large online market

Reichardt, J., & Bornholdt, S. (2007) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/2007/06/p06016 · Cited by 34

Community Assessment Using Evidence Networks

Mitzlaff, F., Atzmueller, M., Benz, D., Andreas Hotho, A., & Gerd Stumme, A. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23599-3_5 · Cited by 33

Data-dependent dissimilarity measure: an effective alternative to geometric distance measures

Aryal, S., Ting, K. M., Washio, T., & Haffari, G. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-017-1046-0 · Cited by 33

Hyperspectral UAV-imagery and photogrammetric canopy height model in estimating forest stand variables

Tuominen, S., Balázs, A., Honkavaara, E., Pölönen, I., Saari, H. K., Hakala, T., & Viljanen, N. (2017) · Silva Fennica · doi:10.14214/sf.7721 · Cited by 33

Wearable Internet-of-Things platform for human activity recognition and health care

Iqbal, A., Ullah, F., Anwar, H., Rehman, A. U., Shah, K., Baig, A., Ali, S., Yoo, S., & Kwak, K. S. (2020) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147720911561 · Cited by 33

A coherence-based approach for the pattern recognition of time series

Maharaj, E. A., & D’Urso, P. (2010) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2010.03.051 · Cited by 33

Heat pump detection from coarse grained smart meter data with positive and unlabeled learning

Fei, H., Kim, Y.-H., Sahu, S., Naphade, M., Mamidipalli, S. K., & Hutchinson, J. (2013) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2488203 · Cited by 33

Linear dimensionality reduction for classification via a sequential Bayes error minimisation with an application to flow meter diagnostics

Gyamfi, K. S., Brusey, J., Hunt, A., & Gaura, E. I. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.09.010 · Cited by 33

The Clever Hans Effect in Anomaly DetectionPreprint

Kauffmann, J., Ruff, L., Montavon, G., & Müller, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.10609 · Cited by 39

COID: A cluster–outlier iterative detection approach to multi-dimensional data analysis

Shi, Y., & Zhang, L. (2010) · Knowledge and Information Systems · doi:10.1007/s10115-010-0323-y · Cited by 32

Similarity Join Processing on Uncertain Data Streams

Lian, X., & Chen, L. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.208 · Cited by 32

Adaptive Cluster Distance Bounding for High-Dimensional Indexing

Ramaswamy, S., & Rose, K. H. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2010.59 · Cited by 32

Comparison of computational intelligence models for cuttings transport in horizontal and deviated wells

Ülker, E., & Sorgun, M. (2016) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2016.07.022 · Cited by 32

Measuring Dependence with Matrix-based Entropy Functional

Yu, S., Alesiani, F., Yu, X., Jenssen, R., & Príncipe, J. C. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17288 · Cited by 32

Contrasting performance of Lidar and optical texture models in predicting avian diversity in a tropical mountain forest

Wallis, C. I. B., Paulsch, D., Zeilinger, J., Silva, B., Fernández, G. F. C., Brandl, R., Farwig, N., & Bendix, J. (2015) · Remote Sensing of Environment · doi:10.1016/j.rse.2015.12.019 · Cited by 32

Approximate searches: k-neighbors + precision

Berrani, S.-A., Amsaleg, L., & Gros, P. (2003) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/956863.956870 · Cited by 32

GAMer: a synthesis of subspace clustering and dense subgraph mining

Günnemann, S., Färber, I., Boden, B., & Seidl, T. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-013-0640-z · Cited by 31

Predicting missing values with biclustering: A coherence-based approach

de França, F. O., Coelho, G. P., & Zuben, F. J. V. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.10.022 · Cited by 31

Using Betweenness Centrality to Identify Manifold Shortcuts

Cukierski, W. J., & Foran, D. J. (2008) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2008.39 · Cited by 31

Clustering

Lam, D., & Wunsch, D. C. (2014) · Academic press library in signal processing · doi:10.1016/b978-0-12-396502-8.00020-6 · Cited by 31

A novel nearest interest point classifier for offline Tamil handwritten character recognition

Deepa, R. N. A., & Rao, R. R. (2019) · Pattern Analysis and Applications · doi:10.1007/s10044-018-00776-x · Cited by 31

Unsupervised Progressive Learning and the STAM Architecture

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Measuring and Explaining the Inter-Cluster Reliability of Multidimensional Projections

Jeon, H., Ko, H.-K., Jo, J., Kim, Y., & Seo, J. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114833 · Cited by 31

Pivot selection: Dimension reduction for distance-based indexing

Mao, R., Miranker, W. L., & Miranker, D. P. (2011) · Journal of Discrete Algorithms · doi:10.1016/j.jda.2011.10.004 · Cited by 30

MSSQ: Manhattan Spatial Skyline Queries

Son, W., Hwang, S.-W., & Hee-kap Ahn, A. (2013) · Information Systems · doi:10.1016/j.is.2013.10.001 · Cited by 30

Survey of Clustering

Greenlaw, R., & Kantabutra, S. (2013) · International Journal of Information Retrieval Research · doi:10.4018/ijirr.2013040101 · Cited by 30

Metamodel-based global optimization using fuzzy clustering for design space reduction

Li, Y., Liu, L., Long, T., & Dong, W.-L. (2013) · Chinese Journal of Mechanical Engineering · doi:10.3901/cjme.2013.05.928 · Cited by 30

Deep autoencoders for attribute preserving face de-identification

Nousi, P., Papadopoulos, S., Tefas, A., & Pitas, I. (2019) · Signal Processing Image Communication · doi:10.1016/j.image.2019.115699 · Cited by 30

Large‐scale tandem mass spectrum clustering using fast nearest neighbor searching

Bittremieux, W., Laukens, K., Noble, W. S., & Dorrestein, P. C. (2021) · Rapid Communications in Mass Spectrometry · doi:10.1002/rcm.9153 · Cited by 31

Two-stage framework for a topology-based projection and visualization of classified document collections

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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. (2005) · IEEE Symposium on Information Visualization · doi:10.1109/infvis.2004.71 · Cited by 30

High dimensional nearest neighbor searching

Ferhatosmanoğlu, H., Tuncel, E., Agrawal, D., & Abbadi, A. E. (2005) · Information Systems · doi:10.1016/j.is.2005.01.001 · Cited by 30

Rate–Distortion Approach to Databases: Storage and Content-Based Retrieval

Tuncel, E., Koulgi, P., & Rose, K. H. (2004) · IEEE Transactions on Information Theory · doi:10.1109/tit.2004.828068 · Cited by 30

On Similarity Measures for Multimedia Database Applications

Candan, K. S., & Li, W. (2001) · Knowledge and Information Systems · doi:10.1007/pl00011658 · Cited by 30

Spatial Data Management

Mamoulis, N. (2011) · Synthesis lectures on data management · doi:10.2200/s00394ed1v01y201111dtm021 · Cited by 30

Classes are Not Clusters: Improving Label-Based Evaluation of Dimensionality Reduction

Jeon, H., Kuo, Y.-H., Aupetit, M. J. M., Ma, K., & Seo, J. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3327187 · Cited by 29

Semi-Random Projection for Dimensionality Reduction and Extreme Learning Machine in High-Dimensional Space

Zhao, R., & Mao, K. (2015) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2015.2437316 · Cited by 29

A C-DBSCAN Algorithm for Determining Bus-Stop Locations Based on Taxi GPS Data

Wang, W., Tao, L., Gao, C., Wang, B., Yang, H. S., & Zhang, Z. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-14717-8_23 · Cited by 29

MSIDX: Multi-Sort Indexing for Efficient Content-Based Image Search and Retrieval

Tiakas, E., Rafailidis, D., Dimou, A., & Daras, P. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2247989 · Cited by 29

A new type of distance metric and its use for clustering

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Adaptive modulation and coding in underwater acoustic communications: a machine learning perspective

Huang, L., Zhang, Q., Tan, W., Wang, Y., Zhang, L., He, C., & Tian, Z. (2020) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-020-01818-x · Cited by 29

Enlarging Applicability Domain of Quantitative Structure–Activity Relationship Models through Uncertainty-Based Active Learning

Zhong, S., Lambeth, D. R., Igou, T., & Chen, Y. (2022) · ACS ES&T Engineering · doi:10.1021/acsestengg.1c00434 · Cited by 29

Understanding and defending against White-box membership inference attack in deep learning

Wu, D., Qi, S., Qi, Y., Li, Q., Cai, B., Guo, Q., & Cheng, J. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.110014 · Cited by 31

Methodology for Behavioral-based Malware Analysis and Detection Using Random Projections and K-Nearest Neighbors Classifiers

Hegedus, J., Miché, Y., Ilin, A., & Lendasse, A. (2011) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/cis.2011.227 · Cited by 29

pPOP: Fast yet accurate parallel hierarchical clustering using partitioning

Dash, M., Petrutiu, S., & Scheuermann, P. (2006) · Data & Knowledge Engineering · doi:10.1016/j.datak.2006.07.004 · Cited by 29

Importance Sampling for a Monte Carlo Matrix Multiplication Algorithm, with Application to Information Retrieval

Eriksson-Bique, S. D., Solbrig, M., Stefanelli, M., Warkentin, S., Abbey, R., & Ipsen, I. C. F. (2011) · SIAM Journal on Scientific Computing · doi:10.1137/10080659x · Cited by 29

SCHISM: a new approach to interesting subspace mining

Sequeira, K., & Zaki, M. J. (2005) · International Journal of Business Intelligence and Data Mining · doi:10.1504/ijbidm.2005.008360 · Cited by 29

Locality Versus Globality: Query-Driven Localized Linear Models for Facial Image Computing

Fu, Y., Zhu Li, B., Yuan, J., Wu, Y., & Huang, T. S. (2008) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2008.2004933 · Cited by 29

Advanced Cluster Analysis

Han, J., Kamber, M., & Pei, J. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-381479-1.00011-3 · Cited by 29

AEkNN: An AutoEncoder kNN–Based Classifier With Built-in Dimensionality Reduction

Pulgar, F. J., Charte, F., Rivera, A. J., & del Jesús, M. J. (2019) · International Journal of Computational Intelligence Systems · doi:10.2991/ijcis.2018.125905686 · Cited by 31

Clustering Neural Patterns in Kernel Reinforcement Learning Assists Fast Brain Control in Brain-Machine Interfaces

Zhang, X., Libedinsky, C., So, R. Q., Príncipe, J. C., & Wang, Y. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2934176 · Cited by 30

Formulating context-dependent similarity functions

Wu, G., Chang, E. Y., & Panda, N. (2005) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1101149.1101307 · Cited by 29

Similarity search in sets and categorical data using the signature tree

Mamoulis, N., Cheung, D. W.-L., & Lian, W. (2004) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde.2003.1260783 · Cited by 29

Learning text classifier using the domain concept hierarchy

Bingjie, W. A. N. G., McKay, B., Abbass, H. A., & Barlow, M. (2003) · International Conference on Communications, Circuits and Systems · doi:10.1109/icccas.2002.1179005 · Cited by 29

A retrieval technique for high-dimensional data and partially specified queries

Orlandić, R., & Yu, B. (2002) · Data & Knowledge Engineering · doi:10.1016/s0169-023x(02)00023-x · Cited by 29

INDEXING TIME-SERIES UNDER CONDITIONS OF NOISE

Vlachos, M., Gunopulos, D., & Das, G. (2004) · Series in machine perception and artificial intelligence · doi:10.1142/9789812565402_0004 · Cited by 29

Enhancing cluster analysis via topological manifold learning

Herrmann, M., Kazempour, D., Scheipl, F., & Kröger, P. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00980-2 · Cited by 36

Sparse Proximal Support Vector Machines for feature selection in high dimensional datasets

Pappu, V., Panagopoulos, O. P., Xanthopoulos, P., & Pardalos, P. M. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.08.022 · Cited by 28

Image Mining Using Association Rule for Medical Image Dataset

Deshmukh, J., & Bhosle, U. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.05.196 · Cited by 29

Movement patterns of a particle swarm in high dimensional spaces

Oldewage, E. T., Engelbrecht, A. P., & Cleghorn, C. W. (2019) · Information Sciences · doi:10.1016/j.ins.2019.09.057 · Cited by 28

Student assessment in cybersecurity training automated by pattern mining and clustering

Švábenský, V., Vykopal, J., Čeleda, P., Tkáčik, K., & Popovič, D. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10954-4 · Cited by 28

Parameter-free and domain-independent similarity search with diversity

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Finding density-based subspace clusters in graphs with feature vectors

Günnemann, S., Boden, B., & Seidl, T. (2012) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-012-0272-z · Cited by 28

Generative Data Models for Validation and Evaluation of Visualization Techniques

Schulz, C., Nocaj, A., El‐Assady, M., Frey, S., Hlawatsch, M., Hund, M., Karch, G. K., Netzel, R., Schätzle, C., Butt, M., et al. (2016) · Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization · doi:10.1145/2993901.2993907 · Cited by 28

Harvey flooding rescue in social media

Yang, Z., Nguyen, L. H., Stuve, J., Cao, G., & Jin, F. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258166 · Cited by 28

Prototype Discovery Using Quality-Diversity

Hagg, A., Asteroth, A., & Bäck, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99253-2_40 · Cited by 28

Unsupervised learning with normalised data and non-Euclidean norms

Doherty, K. A. J., Adams, R. G., & Davey, N. (2005) · Applied Soft Computing · doi:10.1016/j.asoc.2005.05.005 · Cited by 28

Clustering high dimension, low sample size data using the maximal data piling distance

Ahn, J., Lee, M., & Yoon, Y. J. (2011) · Statistica Sinica · doi:10.5705/ss.2010.148 · Cited by 28

The Big Picture

Gori, M. (2017) · Elsevier eBooks · doi:10.1016/b978-0-08-100659-7.00001-4 · Cited by 27

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

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