{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T03:35:02Z","timestamp":1768448102911,"version":"3.49.0"},"reference-count":69,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"USGS EROS","award":["SA2500150"],"award-info":[{"award-number":["SA2500150"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Efficient clustering of high-spatial-dimensional satellite image datasets remains a critical challenge, particularly due to the computational demands of spectral distance calculations, random centroid initialization, and sensitivity to outliers in conventional K-Means algorithms. This study presents a comprehensive comparative analysis of eight parallelized variants of the K-Means algorithm, designed to enhance clustering efficiency and reduce computational burden for large-scale satellite image analysis. The proposed parallelized implementations incorporate optimized centroid initialization for better starting point selection using a dynamic K-Means sharp method to detect the outlier to improve cluster robustness, and a Nearest-Neighbor Iteration Calculation Reduction method to minimize redundant computations. These enhancements were applied to a test set of 114 global land cover data cubes, each comprising high-dimensional satellite images of size 3712 \u00d7 3712 \u00d7 16 and executed on multi-core CPU architecture to leverage extensive parallel processing capabilities. Performance was evaluated across three criteria: convergence speed (iterations), computational efficiency (execution time), and clustering accuracy (RMSE). The Parallelized Enhanced K-Means (PEKM) method achieved the fastest convergence at 234 iterations and the lowest execution time of 4230 h, while maintaining consistent RMSE values (0.0136) across all algorithm variants. These results demonstrate that targeted algorithmic optimizations, combined with effective parallelization strategies, can improve the practicality of K-Means clustering for high-dimensional-satellites image analysis. This work underscores the potential of improving K-Means clustering frameworks beyond hardware acceleration alone, offering scalable solutions good for large-scale unsupervised image classification tasks.<\/jats:p>","DOI":"10.3390\/a18080532","type":"journal-article","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T15:19:02Z","timestamp":1755789542000},"page":"532","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving K-Means Clustering: A Comparative Study of Parallelized Version of Modified K-Means Algorithm for Clustering of Satellite Images"],"prefix":"10.3390","volume":"18","author":[{"given":"Yuv Raj","family":"Pant","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, South Dakota State University (SDSU), Brooking, SD 57007, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0836-4768","authenticated-orcid":false,"given":"Larry","family":"Leigh","sequence":"additional","affiliation":[{"name":"Image Processing Lab, Engineering Office of Research, South Dakota State University (SDSU), Brooking, SD 57007, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juliana","family":"Fajardo Rueda","sequence":"additional","affiliation":[{"name":"Image Processing Lab, Engineering Office of Research, South Dakota State University (SDSU), Brooking, SD 57007, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,21]]},"reference":[{"key":"ref_1","unstructured":"Ao, S.I. (2012). World Congress on Engineering: WCE 2012: 4\u20136 July, 2012, Imperial College London, London, U.K., Newswood Ltd.; International Association of Engineers."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"113195","DOI":"10.1016\/j.rse.2022.113195","article-title":"Fifty Years of Landsat Science and Impacts","volume":"280","author":"Wulder","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105295","DOI":"10.1016\/j.cageo.2022.105295","article-title":"Remote Sensing Scene Classification under Scarcity of Labelled Samples\u2014A Survey of the State-of-the-Arts","volume":"171","author":"Dutta","year":"2023","journal-title":"Comput. Geosci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Shrestha, M., Leigh, L., and Helder, D. (2019). Classification of North Africa for Use as an Extended Pseudo Invariant Calibration Sites (EPICS) for Radiometric Calibration and Stability Monitoring of Optical Satellite Sensors. Remote Sens. Environ., 11.","DOI":"10.3390\/rs11070875"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fajardo Rueda, J., Leigh, L., Teixeira Pinto, C., Kaewmanee, M., and Helder, D. (2021). Classification and Evaluation of Extended Pics (Epics) on a Global Scale for Calibration and Stability Monitoring of Optical Satellite Sensors. Remote Sens. Environ., 13.","DOI":"10.3390\/rs13173350"},{"key":"ref_6","first-page":"18671","article-title":"Satellite Imagery Land Cover Classification Using K-Means Clustering Algorithm: Computer Vision for Environmental Information Extraction","volume":"63","author":"Usman","year":"2013","journal-title":"Elixir Int. J. Comput. Sci. Eng."},{"key":"ref_7","unstructured":"Yasin, H.E.E., and Kornel, C. (2024). Evaluating Satellite Image Classification: Exploring Methods and Techniques. Geographic Information Systems-Data Science Approach, IntechOpen."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kaewmanee, M., Leigh, L., Shah, R., and Gross, G. (2023). Inter-Comparison of Landsat-8 and Landsat-9 during On-Orbit Initialization and Verification (OIV) Using Extended Pseudo Invariant Calibration Sites (EPICS): Advanced Methods. Remote Sens., 15.","DOI":"10.3390\/rs15092330"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Shah, R., Leigh, L., Kaewmanee, M., and Pinto, C.T. (2022). Validation of Expanded Trend-to-Trend Cross-Calibration Technique and Its Application to Global Scale. Remote Sens., 14.","DOI":"10.3390\/rs14246216"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yin, L., Lv, L., Wang, D., Qu, Y., Chen, H., and Deng, W. (2023). Spectral Clustering Approach with K-Nearest Neighbor and Weighted Mahalanobis Distance for Data Mining. Electronics, 12.","DOI":"10.3390\/electronics12153284"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ni, L., Manman, P., and Qiang, W. (2024). A Spectral Clustering Algorithm for Non-Linear Graph Embedding in Information Networks. Appl. Sci., 14.","DOI":"10.3390\/app14114946"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ran, X., Xi, Y., Lu, Y., Wang, X., and Lu, Z. (2023). Comprehensive Survey on Hierarchical Clustering Algorithms and the Recent Developments, Springer.","DOI":"10.1007\/s10462-022-10366-3"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, X., Shen, X., and Ouyang, T. (2022). Extension of DBSCAN in Online Clustering: An Approach Based on Three-Layer Granular Models. Appl. Sci., 12.","DOI":"10.3390\/app12199402"},{"key":"ref_14","unstructured":"Dinh, T., Hauchi, W., Lisik, D., Koren, M., Tran, D., Yu, P.S., and Torres-Sospedra, J. (2024). Data Clustering: An Essential Technique in Data Science. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chaudhry, M., Shafi, I., Mahnoor, M., Vargas, D.L.R., Thompson, E.B., and Ashraf, I. (2023). A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective. Symmetry, 15.","DOI":"10.3390\/sym15091679"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Miao, S., Zheng, L., Liu, J., and Jin, H. (2023, January 24). K-Means Clustering Based Feature Consistency Alignment for Label-Free Model Evaluation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00332"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Al-Sabbagh, A., Hamze, K., Khan, S., and Elkhodr, M. (2024). An Enhanced K-Means Clustering Algorithm for Phishing Attack Detections. Electronics, 13.","DOI":"10.3390\/electronics13183677"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Seraj, R., and Islam, S.M.S. (2020). The K-Means Algorithm: A Comprehensive Survey and Performance Evaluation. Electronics, 9.","DOI":"10.3390\/electronics9081295"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.ins.2022.11.139","article-title":"K-Means Clustering Algorithms: A Comprehensive Review, Variants Analysis, and Advances in the Era of Big Data","volume":"622","author":"Ikotun","year":"2023","journal-title":"Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101386","DOI":"10.1016\/j.iot.2024.101386","article-title":"Hierarchical Semi-Supervised Approach for Classifying Activities of Workers Utilising Indoor Trajectory Data","volume":"28","author":"Rana","year":"2024","journal-title":"Internet Things"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/S0169-023X(02)00138-6","article-title":"Fast Hierarchical Clustering and Its Validation","volume":"44","author":"Dash","year":"2003","journal-title":"Data Knowl. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Shi, K., Yan, J., and Yang, J. (2024). A Semantic Partition Algorithm Based on Improved K-Means Clustering for Large-Scale Indoor Areas. ISPRS Int. J. Geoinf., 13.","DOI":"10.3390\/ijgi13020041"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1016\/j.ins.2022.06.013","article-title":"Efficient Density and Cluster Based Incremental Outlier Detection in Data Streams","volume":"607","author":"Degirmenci","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1016\/j.patrec.2019.10.019","article-title":"Fast and General Density Peaks Clustering","volume":"128","author":"Sieranoja","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_25","unstructured":"Guava (2025, April 15). Spectral Clustering for Large Scale Datasets (Part 1). Available online: https:\/\/medium.com\/@guava1427\/spectral-clustering-for-large-scale-datasets-part-1-874571887610."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1186\/s40537-017-0087-2","article-title":"Clustering Large Datasets Using K-Means Modified Inter and Intra Clustering (KM-I2C) in Hadoop","volume":"4","author":"Sreedhar","year":"2017","journal-title":"J. Big Data"},{"key":"ref_27","unstructured":"Cap\u00f3, M., P\u00e9rez, A., and Lozano, J.A. (2018). An Efficient K -Means Clustering Algorithm for Massive Data. arXiv."},{"key":"ref_28","unstructured":"Jin, S., Cui, Y., and Yu, C. (2016). A New Parallelization Method for K-Means. arXiv."},{"key":"ref_29","unstructured":"Honggang, W., Jide, Z., Hongguang, L., and Jianguo, W. (2008, January 12\u201314). Parallel Clustering Algorithms for Image Processing on Multi-Core CPUs. Proceedings of the International Conference on Computer Science and Software Engineering (CSSE), Wuhan, China."},{"key":"ref_30","unstructured":"Zhang, Y., Xiong, Z., Mao, J., and Ou, L. (2006, January 21\u201323). The Study of Parallel K-Means Algorithm. Proceedings of the 2006 6th World Congress on Intelligent Control and Automation, Dalian, China."},{"key":"ref_31","unstructured":"Macqueen, J. (July, January 21). SOME METHODS FOR CLASSIFICATION AND ANALYSIS OF MULTIVARIATE OBSERVATIONS. Proceedings of the Berkeley Symposium on Mathematical Statistics & Probability, Berkeley, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Parveen, S., and Yang, M. (2025). Lasso-Based k-Means++ Clustering. Electronics, 14.","DOI":"10.3390\/electronics14071429"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7086878","DOI":"10.1155\/2024\/7086878","article-title":"K-Means Centroids Initialization Based on Differentiation Between Instances Attributes","volume":"2024","author":"Khan","year":"2024","journal-title":"Int. J. Intell. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chan, J.Y.K., Leung, A.P., and Xie, Y. (2021). Efficient High-Dimensional Kernel k-Means++ with Random Projection. Appl. Sci., 11.","DOI":"10.3390\/app11156963"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4743","DOI":"10.1007\/s10489-018-1238-7","article-title":"K-Means Properties on Six Clustering Benchmark Datasets","volume":"48","author":"Sieranoja","year":"2018","journal-title":"Appl. Intell."},{"key":"ref_36","first-page":"1","article-title":"Adaptively Robust and Sparse K-Means Clustering","volume":"2024","author":"Li","year":"2024","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/RoboMech.2017.8261116","article-title":"K-Means-Sharp: Modified Centroid Update for Outlier-Robust k-Means Clustering","volume":"Volume 2018-January","author":"Olukanmi","year":"2017","journal-title":"Proceedings of the 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech)"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhao, J., Bao, Y., Li, D., and Guan, X. (2024). An Improved K-Means Algorithm Based on Contour Similarity. Mathematics, 12.","DOI":"10.3390\/math12142211"},{"key":"ref_39","first-page":"125287","article-title":"ResAD: A Simple Framework for Class Generalizable Anomaly Detection","volume":"37","author":"Yao","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wu, S., Zhai, Y., Liu, J., Huang, J., Jian, Z., Dai, H., Di, S., Chen, Z., and Cappello, F. (2024). TurboFFT: A High-Performance Fast Fourier Transform with Fault Tolerance on GPU. arXiv.","DOI":"10.1109\/CLUSTER59578.2024.00035"},{"key":"ref_41","unstructured":"Shi, N., Liu, X., and Guan, Y. (2010, January 2\u20134). Research on K-Means Clustering Algorithm: An Improved k-Means Clustering Algorithm. Proceedings of the 3rd International Symposium on Intelligent Information Technology and Security Informatics (IITSI), Jian, China."},{"key":"ref_42","unstructured":"Wang, J., Wang, J., Ke, Q., Zeng, G., Li, S., and Valley, S. (2012, January 16\u201321). Fast Approximate K-Means via Cluster Closures. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1049\/sfw2.12032","article-title":"An Improved K-Means Algorithm for Big Data","volume":"16","author":"Moodi","year":"2022","journal-title":"IET Softw."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Mussabayev, R., and Mussabayev, R. (2024, January 15\u201318). Superior Parallel Big Data Clustering Through Competitive Stochastic Sample Size Optimization in Big-Means. Proceedings of the Asian Conference on Intelligent Information and Database Systems, Ras Al Khaimah, United Arab Emirates.","DOI":"10.1007\/978-981-97-4985-0_18"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1016\/j.procs.2016.06.025","article-title":"An Efficient Parallel Block Processing Approach for K -Means Algorithm for High Resolution Orthoimagery Satellite Images","volume":"Volume 89","author":"Rashmi","year":"2016","journal-title":"Proceedings of the Procedia Computer Science"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1109\/TKDE.2005.18","article-title":"Shared Memory Parallelization of Data Mining Algorithms: Techniques, Programming Interface, and Performance","volume":"17","author":"Jin","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.compeleceng.2017.12.002","article-title":"A GPU-Accelerated Parallel K-Means Algorithm","volume":"75","author":"Cuomo","year":"2019","journal-title":"Comput. Electr. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Bellavita, J., Pasquali, T., Del Rio Martin, L., Vella, F., and Guidi, G. (2025). Popcorn: Accelerating Kernel K-Means on GPUs Through Sparse Linear Algebra, Association for Computing Machinery.","DOI":"10.1145\/3710848.3710887"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_50","unstructured":"Dickinson, R.E., Henderson-Sellers, A., Kennedy, P.J., and Wilson, M.F. (1986). NCAR\/TN-257+STR Biosphere-Atmosphere Transfer Scheme (BATS) for the NCAR Community Climate Model, National Center for Atmospheric Research."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Shahapure, K.R., and Nicholas, C. (2020, January 6\u20139). Cluster Quality Analysis Using Silhouette Score. Proceedings of the 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), Sydney, Australia.","DOI":"10.1109\/DSAA49011.2020.00096"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"571","DOI":"10.33395\/sinkron.v9i1.13246","article-title":"Determining The Optimal Number of K-Means Clusters Using The Calinski Harabasz Index and Krzanowski and Lai Index Methods for Groupsing Flood Prone Areas In North Sumatra","volume":"9","author":"Syahputri","year":"2024","journal-title":"Sinkron"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"KC, M., Leigh, L., Pinto, C.T., and Kaewmanee, M. (2023). Method of Validating Satellite Surface Reflectance Product Using Empirical Line Method. Remote Sens., 15.","DOI":"10.3390\/rs15092240"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"24","DOI":"10.3934\/aci.2024003","article-title":"Clustering Accuracy","volume":"4","author":"Sieranoja","year":"2024","journal-title":"Appl. Comput. Intell."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"102712","DOI":"10.1016\/j.simpat.2022.102712","article-title":"Parallel Random Swap: An Efficient and Reliable Clustering Algorithm in Java","volume":"124","author":"Nigro","year":"2023","journal-title":"Simul. Model. Pract. Theory"},{"key":"ref_56","first-page":"1","article-title":"Efficiency of Random Swap Clustering","volume":"5","year":"2018","journal-title":"J. Big Data"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1109\/83.855429","article-title":"A Fast Exact GLA Based on Code Vector Activity Detection","volume":"9","author":"Kaukoranta","year":"2000","journal-title":"IEEE Trans. Image Process."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Fajardo Rueda, J., Leigh, L., Kaewmanee, M., Byregowda, H., and Teixeira Pinto, C. (2025). Derivation of Hyperspectral Profiles for Global Extended Pseudo Invariant Calibration Sites (EPICS) and Their Application in Satellite Sensor Cross-Calibration. Remote Sens., 17.","DOI":"10.3390\/rs17020216"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Fajardo Rueda, J., Leigh, L., and Teixeira Pinto, C. (2024). Identification of Global Extended Pseudo Invariant Calibration Sites (EPICS) and Their Validation Using Radiometric Calibration Network (RadCalNet). Remote Sens., 16.","DOI":"10.3390\/rs16224129"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.gltp.2021.01.002","article-title":"Development of Classification System for LULC Using Remote Sensing and GIS","volume":"2","author":"Alshari","year":"2021","journal-title":"Glob. Transit. Proc."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"012036","DOI":"10.1088\/1757-899X\/790\/1\/012036","article-title":"Accelerating K-Means on GPU with CUDA Programming","volume":"790","author":"Yang","year":"2020","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Han, S., and Lee, J. (2024). Parallelized Inter-Image k-Means Clustering Algorithm for Unsupervised Classification of Series of Satellite Images. Remote Sens., 16.","DOI":"10.3390\/rs16010102"},{"key":"ref_63","unstructured":"Andoni, A., Indyk, P., and Razenshteyn, I. (2018, January 1\u20139). Approximate Nearest Neighbor Search in High Dimensions. Proceedings of the International Congress of Mathematicians (ICM), Rio de Janeiro, Brazil."},{"key":"ref_64","unstructured":"Shindler, M., Wong, A., and Meyerson, A. (2011, January 10\u201313). Fast and Accurate \u03ba-Means for Large Datasets. Proceedings of the 25th Annual Conference on Neural Information Processing Systems (NIPS), Granada, Spain."},{"key":"ref_65","unstructured":"Spalding-Jamieson, J., Robson, E.W., and Zheng, D.W. (2025). Scalable K-Means Clustering for Large k via Seeded Approximate Nearest-Neighbor Search. arXiv."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"11897","DOI":"10.1109\/ACCESS.2018.2810267","article-title":"Clustering Approach Based on Mini Batch Kmeans for Intrusion Detection System over Big Data","volume":"6","author":"Peng","year":"2018","journal-title":"IEEE Access"},{"key":"ref_67","unstructured":"Jourdan, B., and Schwartzman, G. (2024). Mini-Batch Kernel k-Means. arXiv."},{"key":"ref_68","first-page":"1","article-title":"Nested Mini-Batch K-Means","volume":"29","author":"Newling","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1007\/3-540-44503-X_27","article-title":"On the Surprising Behavior of Distance Metrics in High Dimensional Space","volume":"Volume 1973","author":"Aggarwal","year":"2001","journal-title":"Database Theory ICDT 2001"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/8\/532\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:32:59Z","timestamp":1760034779000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/8\/532"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,21]]},"references-count":69,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["a18080532"],"URL":"https:\/\/doi.org\/10.3390\/a18080532","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,21]]}}}