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 45 articles )

Showing results 1 to 20 of 45.
Sorted by year (citations)

1 2 3 next

  1. Dey, Tamal K.; Shi, Dayu; Wang, Yusu: SimBa: an efficient tool for approximating Rips-filtration persistence via Simplicial Batch collapse (2019)
  2. Michael Hahsler; Matthew Piekenbrock; Derek Doran: dbscan: Fast Density-Based Clustering with R (2019) not zbMATH
  3. Zhong, Sikai; Zhong, Zichun; Hua, Jing: Surface reconstruction by parallel and unified particle-based resampling from point clouds (2019)
  4. Buchet, Mickaël; Chazal, Frédéric; Oudot, Steve Y.; Sheehy, Donald R.: Efficient and robust persistent homology for measures (2016)
  5. Dey, Tamal K.; Shi, Dayu; Wang, Yusu: SimBa: an efficient tool for approximating Rips-filtration persistence via simplicial batch-collapse (2016)
  6. Lewis, Allison; Smith, Ralph; Williams, Brian; Figueroa, Victor: An information theoretic approach to use high-fidelity codes to calibrate low-fidelity codes (2016)
  7. Neagoe, Angela; Popa, Radu: Slope failure probability at critical value of safety factor (2016)
  8. Xiao, Bo; Biros, George: Parallel algorithms for nearest neighbor search problems in high dimensions (2016)
  9. Cavoretto, Roberto: A numerical algorithm for multidimensional modeling of scattered data points (2015)
  10. Diehl, Patrick; Schweitzer, Marc Alexander: Efficient neighbor search for particle methods on GPUs (2015)
  11. Boissonnat, Jean-Daniel; Maria, Clément: The simplex tree: an efficient data structure for general simplicial complexes (2014)
  12. 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) ioport
  13. Emiris, Ioannis Z.; Fisikopoulos, Vissarion: Efficient random-walk methods for approximating polytope volume (2014)
  14. Lim, Keng-Wit; Krabbenhoft, Kristian; Andrade, José E.: A contact dynamics approach to the granular element method (2014)
  15. 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) ioport
  16. Leiva, Luis A.; Vidal, Enrique: Warped (K)-means: an algorithm to cluster sequentially-distributed data (2013) ioport
  17. Millán, Daniel; Rosolen, Adrian; Arroyo, Marino: Nonlinear manifold learning for meshfree finite deformation thin-shell analysis (2013)
  18. Glaser, Judith; Heider, Pascal: Arbitrage-free approximation of call price surfaces and input data risk (2012)
  19. Hemmer, Michael; Kleinbort, Michal; Halperin, Dan: Improved implementation of point location in general two-dimensional subdivisions (2012)
  20. Samuel Gerber; Kristin Potter: Data Analysis with the Morse-Smale Complex: The msr Package for R (2012) not zbMATH

1 2 3 next