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An efficient k nearest neighbors searching algorithm for a query line

Subhas C. Nandy, Sandip Das, Partha P. Goswami
2003 Theoretical Computer Science  
We present an algorithm for ÿnding k nearest neighbors of a given query line among a set of n points distributed arbitrarily on a two-dimensional plane.  ...  Our algorithm requires O(n 2 ) time and O(n 2 =log n) space to preprocess the given set of points, and it answers the query for a given line in O(k + log n) time, where k may also be an input at the query  ...  Harayama for helpful discussions. The critical comments and suggestions given by the referees helped the authors to improve the presentation of the paper.  ... 
doi:10.1016/s0304-3975(02)00322-5 fatcat:kamdg5o4ibgovjrbdzlvsm2s2i

Efficient k-nearest neighbor searches for multi-source forest attribute mapping

Andrew O. Finley, Ronald E. McRoberts
2008 Remote Sensing of Environment  
In this study, we explore the utility of data structures that facilitate efficient nearest neighbor searches for application in multi-source forest attribute prediction.  ...  Further, given our trial data, we found that enormous gain in search time efficiency, afforded by approximate nearest neighbor search algorithms, does not result in compromised kNN prediction.  ...  One disadvantage of the method, limiting its utility, is the computationally intensive search for the nearest neighbor subset. For a small number of queries, search time is not an issue.  ... 
doi:10.1016/j.rse.2007.08.024 fatcat:llgrkpcizfha5j6u42mct76tem

Survey of Nearest Neighbor Techniques [article]

Nitin Bhatia, Vandana
2010 arXiv   pre-print
Weighted kNN, Model based kNN, Condensed NN, Reduced NN, Generalized NN are structure less techniques whereas k-d tree, ball tree, Principal Axis Tree, Nearest Feature Line, Tunable NN, Orthogonal Search  ...  The nearest neighbor (NN) technique is very simple, highly efficient and effective in the field of pattern recognition, text categorization, object recognition etc.  ...  Center based Nearest Neighbor (CNN) [27] A Center Line is calculated 1.Highly efficient for small data sets 1.  ... 
arXiv:1007.0085v1 fatcat:uwac2xtmhnajvoxjrhjnsf3xwy

The ANN-tree: an index for efficient approximate nearest neighbor search

King-Ip Lin, Congjun Yang
2001 Proceedings Seventh International Conference on Database Systems for Advanced Applications DASFAA 2001 DASFAA-01  
This m a k e s the A N N -t r e e a preferable i n d e x structure f o r both exact and approximate nearest neighbor searches.  ...  E v e n if a n exact nearest nearest neighbor query is desired, t h e A N N -t r e e i s demonstrably m o r e efficient t h a n existing structures like t h e R *-tree.  ...  QED The above theorem provides a sufficient condition for an index to have a correct minimum access nearest neighbor search algorithm.  ... 
doi:10.1109/dasfaa.2001.916376 dblp:conf/dasfaa/LinY01 fatcat:7bnurof2trbpjl6xrfwzgk43u4

A Simple Framework for the Generalized Nearest Neighbor Problem [chapter]

Tomas Hruz, Marcel Schöngens
2012 Lecture Notes in Computer Science  
The problem of finding a nearest neighbor from a set of points in R d to a complex query object has attracted considerable attention due to various applications in computational geometry, bio-informatics  ...  We propose a generic method that solves the problem for various classes of query objects and distance functions in a unified way.  ...  We are also grateful to Jiri Matoušek for several discussions on the duality, arrangements and tricky details.  ... 
doi:10.1007/978-3-642-31155-0_8 fatcat:ejknzg4z7jcc3ekeimlu6g24te

Continuous All k-Nearest-Neighbor Querying in Smartphone Networks

Georgios Chatzimilioudis, Demetrios Zeinalipour-Yazti, Wang-Chien Lee, Marios D. Dikaiakos
2012 2012 IEEE 13th International Conference on Mobile Data Management  
Consider a centralized query operator that identifies to every smartphone user its k geographically nearest neighbors at all times, a query we coin Continuous All k-Nearest Neighbor (CAkNN).  ...  We introduce an algorithm, coined Proximity, which answers CAkNN queries in O(n(k + λ)) time, where n denotes the number of users and λ a network-specific parameter (λ << n).  ...  Manolis Spanakis for the help with the Manhattan dataset.  ... 
doi:10.1109/mdm.2012.19 dblp:conf/mdm/ChatzimilioudisZLD12 fatcat:odzj4o4zznh5bh3yr6nxfkrime

kANN on the GPU with Shifted Sorting [article]

Shengren Li, Lance Simons, Jagadeesh Bhaskar Pakaravoor, Fatemeh Abbasinejad, John D. Owens, Nina Amenta
2012 High Performance Graphics  
We describe the implementation of a simple method for finding k approximate nearest neighbors (ANNs) on the GPU.  ...  Irrespective of the distribution and also roughly of the size of the set of input data points, we can find 50 ANNs for 1M queries at a rate of about 1200 queries/ms.  ...  We would also like to thank Andrew Davidson and Anjul Patney for their ideas and insight.  ... 
doi:10.2312/eggh/hpg12/039-047 fatcat:mo263xa6z5f2hbejx7u62egjve

Optimal multi-step k-nearest neighbor search

Thomas Seidl, Hans-Peter Kriegel
1998 Proceedings of the 1998 ACM SIGMOD international conference on Management of data - SIGMOD '98  
After revealing the strong performance shortcomings of the state-of-the-art algorithm for k-nearest neighbor search [Korn et al. 1996], we present a novel multi-step algorithm which is guaranteed to produce  ...  For an increasing number of modern database applications, efficient support of similarity search becomes an important task.  ...  Most of the available algorithms are tuned to efficiently support k-nearest neighbor queries for a fixed retrieval parameter k.  ... 
doi:10.1145/276304.276319 dblp:conf/sigmod/SeidlK98 fatcat:mktlcsod5nhsbaa3licv3cn4wy

An efficient nearest neighbor search in high-dimensional data spaces

Dong-Ho Lee, Hyoung-Joo Kim
2002 Information Processing Letters  
[7] proposed an algorithm for a nearest neighbor search in the R-tree.  ...  Introduction Similarity search in multimedia databases requires an efficient support of nearest neighbor search on a large set of high-dimensional points.  ... 
doi:10.1016/s0020-0190(01)00236-8 fatcat:7folunnj55g7jnbbzk2hpnp3be

Optimal multi-step k-nearest neighbor search

Thomas Seidl, Hans-Peter Kriegel
1998 SIGMOD record  
After revealing the strong performance shortcomings of the state-of-the-art algorithm for k-nearest neighbor search [Korn et al. 1996], we present a novel multi-step algorithm which is guaranteed to produce  ...  For an increasing number of modern database applications, efficient support of similarity search becomes an important task.  ...  Most of the available algorithms are tuned to efficiently support k-nearest neighbor queries for a fixed retrieval parameter k.  ... 
doi:10.1145/276305.276319 fatcat:hbqyc4rlpvctrblbrg2lfyscqa

A Trajectory Privacy Preserving Scheme in the CANNQ Service for IoT

Zhang, Jin, Huang, Fu, Wang
2019 Sensors  
Furthermore, an aggregate nearest neighbor query algorithm based on strategy optimization, is adopted, to minimize the overhead of the LSP.  ...  'Aggregate nearest neighbor query' is a new type of location-based query which asks the question, 'what is the best location for a given group of people to gather?'  ...  Third, it is feasible to improve the response speed of the queries by improving the ANN query algorithm, but there is a lack of an efficient method for computing the aggregate nearest neighbors.  ... 
doi:10.3390/s19092190 fatcat:73wfdz5pqzfplj3foswlzykdly

DART: An Efficient Method for Direction-Aware Bichromatic Reverse k Nearest Neighbor Queries [chapter]

Kyoung-Won Lee, Dong-Wan Choi, Chin-Wan Chung
2013 Lecture Notes in Computer Science  
We formally define the DBRkNN query, and then propose an efficient algorithm, called DART, for processing the DBRkNN query.  ...  We adopt a filter-refinement framework that is widely used in many algorithms for reverse nearest neighbor queries.  ...  objects that have q within k nearest neighbors (k is a positive integer, typically small).  ... 
doi:10.1007/978-3-642-40235-7_17 fatcat:msv24jy6f5extlqrlvw76q66pq

Voronoi Partition to Support Data Search in Uncertain Database with k-Bound Filtering

Slamet Sudaryanto Nurhendratno, Sudaryanto, Solichul Huda
2020 Journal of Computer Science  
For this uncertain database, the important query method is Probabilistic k-Nearest Neighbor query (PkNN), which calculates the probability of the set of k objects to be closest to the given query point  ...  In this study, we propose a method called voronoi partitioning to support searching in uncertain database (Partition threshold k Aggregate Nearest Neighbor query method-Partition_PANN).  ...  Author's Contributions Slamet Sudaryanto Nurhendratno: Is involved in the concept of developing a search method, testing and analyzing results.  ... 
doi:10.3844/jcssp.2020.1753.1764 fatcat:s54uj4ohibdulc7ssqxeii4ozu

Finding Top-k Optimal Sequenced Routes -- Full Version [article]

Huiping Liu, Cheqing Jin, Bin Yang, Aoying Zhou
2018 arXiv   pre-print
In addition, we demonstrate the high extensibility of the proposed algorithms by incorporating Hop Labeling, an effective label indexing technique for shortest path queries, to further improve efficiency  ...  In StarKOSR, we further improve the efficiency by extending routes in an A* manner.  ...  In this way, the i-th nearest neighbor in a category can be identified efficiently in an on-line manner by simply looking up the inverted label index.  ... 
arXiv:1802.08014v1 fatcat:to7mryaz4vbolhcveoba5whdd4

K-NN query algorithm based on PB-tree with the parallel lines division

Jine Tang, ZhangBing Zhou, Qun Wang
2012 Communications in Mobile Computing  
Spatial index and query are enabling techniques for achieving the vision of the Internet of Things. K-NN is an algorithm which is used widely in spatial database.  ...  Based on the study of previous algorithms, this paper proposes a novel K-NN query algorithm based on PB-tree with the parallel lines division.  ...  Acknowledgements This work was supported by the Fundamental Research Funds for the Central Universities (China University of Geosciences at Beijing).  ... 
doi:10.1186/2192-1121-1-10 fatcat:omtmv7uswrevxg74feelncvzke
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