ANN is a library written in C++, which supports data structures and algorithms for both exact and approximate nearest neighbor searching in arbitrarily high dimensions. In the nearest neighbor problem a set of data points in d-dimensional space is given. These points are preprocessed into a data structure, so that given any query point q, the nearest or generally k nearest points of P to q can be reported efficiently. The distance between two points can be defined in many ways. ANN assumes that distances are measured using any class of distance functions called Minkowski metrics. These include the well known Euclidean distance, Manhattan distance, and max distance. Based on our own experience, ANN performs quite efficiently for point sets ranging in size from thousands to hundreds of thousands, and in dimensions as high as 20. (For applications in significantly higher dimensions, the results are rather spotty, but you might try it anyway.) The library implements a number of different data structures, based on kd-trees and box-decomposition trees, and employs a couple of different search strategies. The library also comes with test programs for measuring the quality of performance of ANN on any particular data sets, as well as programs for visualizing the structure of the geometric data structures.

References in zbMATH (referenced in 30 articles )

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  1. Cavoretto, Roberto: A numerical algorithm for multidimensional modeling of scattered data points (2015)
  2. Diehl, Patrick; Schweitzer, Marc Alexander: Efficient neighbor search for particle methods on GPUs (2015)
  3. Cordeiro, Robson L.F.; Guo, Fan; Haverkamp, Donna S.; Horne, James H.; Hughes, Ellen K.; Kim, Gunhee; Romani, Luciana A.S.; Coltri, Priscila P.; Souza, Tamires T.; Traina, Agma J.M.; Traina, Caetano; Faloutsos, Christos: QuMinS: fast and scalable querying, mining and summarizing multi-modal databases (2014)
  4. Lim, Keng-Wit; Krabbenhoft, Kristian; Andrade, José E.: A contact dynamics approach to the granular element method (2014)
  5. Liu, Ruochen; He, Fei; Liu, Jing; Ma, Wenping; Li, Yangyang: A point symmetry-based clonal selection clustering algorithm and its application in image compression (2014)
  6. Leiva, Luis A.; Vidal, Enrique: Warped $K$-means: an algorithm to cluster sequentially-distributed data (2013)
  7. Hemmer, Michael; Kleinbort, Michal; Halperin, Dan: Improved implementation of point location in general two-dimensional subdivisions (2012)
  8. Biau, Gérard; Chazal, Frédéric; Cohen-Steiner, David; Devroye, Luc; Rodríguez, Carlos: A weighted $k$-nearest neighbor density estimate for geometric inference (2011)
  9. Wang, Jun; Yang, Zhouwang; Jin, Liangbing; Deng, Jiansong; Chen, Falai: Parallel and adaptive surface reconstruction based on implicit PHT-splines (2011)
  10. Chen, Lin; Meng, XiangXu: Anisotropic resizing and deformation preserving geometric textures (2010)
  11. Dadvand, Pooyan; Rossi, Riccardo; Oñate, Eugenio: An Object-Oriented Environment For Developing Finite Element Codes For Multi-Disciplinary Applications (2010)
  12. Saha, Sriparna; Bandyopadhyay, Sanghamitra: A symmetry based multiobjective clustering technique for automatic evolution of clusters (2010)
  13. Saha, Sriparna; Bandyopadhyay, Sanghamitra: A new multiobjective clustering technique based on the concepts of stability and symmetry (2010)
  14. Xiao, Chunxia; Nie, Yongwei; Hua, Wei; Zheng, Wenting: Fast multi-scale joint bilateral texture upsampling (2010)
  15. Heider, Pascal: A local least-squares method for solving nonlinear partial differential equations of second order (2009)
  16. Lai, Yu-Kun; Hu, Shi-Min; Martin, Ralph R.; Rosin, Paul L.: Rapid and effective segmentation of 3D models using random walks (2009)
  17. Saha, Sriparna; Bandyopadhyay, Sanghamitra: A new line symmetry distance and its application to data clustering (2009)
  18. Wang, Wencheng; Liu, Feitong; Huang, Peijie; Wu, Enhua: Texture synthesis via the matching compatibility between patches (2009)
  19. Kantor, George; Fairfield, Nathaniel; Jonak, Dominic; Wettergreen, David: Experiments in navigation and mapping with a hovering AUV (2008)
  20. Mohayaee, Roya; Sobolevskiĭ, Andreĭ: The Monge-Ampère-Kantorovich approach to reconstruction in cosmology (2008)

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