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Relevant bibliographies by topics / Search for the nearest neighbour

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Academic literature on the topic 'Search for the nearest neighbour'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Search for the nearest neighbour"

1

Myasnikov, E. "Exact Nearest Neighbour Search within Constrained Neighbourhood Using the Forest of Vp-Tree-Like Structures." Journal of Physics: Conference Series 2096, no. 1 (2021): 012199. http://dx.doi.org/10.1088/1742-6596/2096/1/012199.

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Abstract In this paper, we address the problem of fast nearest neighbour search. Unfortunately, well-known indexing data structures, such as vp-trees perform poorly on some datasets and do not provide significant acceleration compared to the brute force approach. In the paper, we consider an alternative solution, which can be applied if we are not interested in some fraction of distant nearest neighbours. This solution is based on building the forest of vp-tree-like structures and guarantees the exact nearest neighbour search in the epsilon-neighbourhood of the query point.

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N, MINOJINI, GAYATHRI R. KRISHNA, REKHA A, and SOWMIYAA P. "Dynamic Nearest Neighbour Search With Keywords." IJARCCE 4, no. 3 (2015): 580–82. http://dx.doi.org/10.17148/ijarcce.2015.43139.

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Suhaibaha, A., A. A. Rahman, U. Uznir, F. Anton, and D. Mioc. "IMPROVING NEAREST NEIGHBOUR SEARCH IN 3D SPATIAL ACCESS METHOD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W1 (October 26, 2016): 69–73. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w1-69-2016.

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Nearest Neighbour (NN) is one of the important queries and analyses for spatial application. In normal practice, spatial access method structure is used during the Nearest Neighbour query execution to retrieve information from the database. However, most of the spatial access method structures are still facing with unresolved issues such as overlapping among nodes and repetitive data entry. This situation will perform an excessive Input/Output (IO) operation which is inefficient for data retrieval. The situation will become more crucial while dealing with 3D data. The size of 3D data is usuall

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Hooda, Meenakshi, and Sumeet Gill. "Nearest Neighbour Search in k-dSLst Tree." Advances in Science, Technology and Engineering Systems Journal 5, no. 4 (2020): 160–66. http://dx.doi.org/10.25046/aj050419.

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Biswas, Sumana, Sreenatha G. Anavatti, and Matthew A. Garratt. "A Time-Efficient Co-Operative Path Planning Model Combined with Task Assignment for Multi-Agent Systems." Robotics 8, no. 2 (2019): 35. http://dx.doi.org/10.3390/robotics8020035.

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Dealing with uncertainties along with high-efficiency planning for task assignment problem is still challenging, especially for multi-agent systems. In this paper, two frameworks—Compromise View model and the Nearest-Neighbour Search model—are analyzed and compared for co-operative path planning combined with task assignment of a multi-agent system in dynamic environments. Both frameworks are capable of dynamically controlling a number of autonomous agents to accomplish multiple tasks at different locations. Furthermore, these two models are capable of dealing with dynamically changing environ

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Zhao, Ning, Jingyue Xu, and Gang Zhou. "Fault Diagnosis of Centrifugal Fan Based on Grid Search Optimized Voting Weighted KNN." Journal of Physics: Conference Series 2636, no. 1 (2023): 012046. http://dx.doi.org/10.1088/1742-6596/2636/1/012046.

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Abstract Fan plays the roles of the forced draft fan, induced draft fan, primary air fan, seal fan, and powder exhauster. As an important auxiliary of the fossil-fuel power station, its working environment is harsh. Timely and efficient fault diagnosis can effectively reduce equipment failure and shutdown losses, and improve the efficiency of thermal power generation. K-Nearest Neighbour (KNN) has good classification ability for non-stationary data samples. In response to the shortcomings of the traditional KNN algorithm, this paper constructs a fault diagnosis model based on the voting weight

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Suhaibah, A., U. Uznir, F. Anton, D. Mioc, and A. A. Rahman. "3D NEAREST NEIGHBOUR SEARCH USING A CLUSTERED HIERARCHICAL TREE STRUCTURE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 87–93. http://dx.doi.org/10.5194/isprs-archives-xli-b2-87-2016.

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Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of t

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Suhaibah, A., U. Uznir, F. Anton, D. Mioc, and A. A. Rahman. "3D NEAREST NEIGHBOUR SEARCH USING A CLUSTERED HIERARCHICAL TREE STRUCTURE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 87–93. http://dx.doi.org/10.5194/isprsarchives-xli-b2-87-2016.

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Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of t

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Cheung, King Lum, and Ada Wai-Chee Fu. "Enhanced nearest neighbour search on the R-tree." ACM SIGMOD Record 27, no. 3 (1998): 16–21. http://dx.doi.org/10.1145/290593.290596.

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Ali, Mohammed Eunus, Saif-ul-Islam Khan, Sharowar Md Shahriar Khan, and Md Nasim. "Spatio-temporal keyword search for nearest neighbour queries." Journal of Location Based Services 9, no. 2 (2015): 113–37. http://dx.doi.org/10.1080/17489725.2015.1066887.

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Dissertations / Theses on the topic "Search for the nearest neighbour"

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Kibriya, Ashraf Masood. "Fast Algorithms for Nearest Neighbour Search." The University of Waikato, 2007. http://hdl.handle.net/10289/2463.

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The nearest neighbour problem is of practical significance in a number of fields. Often we are interested in finding an object near to a given query object. The problem is old, and a large number of solutions have been proposed for it in the literature. However, it remains the case that even the most popular of the techniques proposed for its solution have not been compared against each other. Also, many techniques, including the old and popular ones, can be implemented in a number of ways, and often the different implementations of a technique have not been thoroughly compared either. This

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Shehu, Usman Gulumbe. "Cube technique for Nearest Neighbour(s) search." Thesis, University of Strathclyde, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248365.

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Casselryd, Oskar, and Filip Jansson. "Troll detection with sentiment analysis and nearest neighbour search." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209474.

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Internet trolls are gaining more influence in society due to the rapidgrowth of social media. A troll farm is a group of Internet trolls that get paid to spread certain opinions or information online. Identifying a troll farm can be difficult, since the trolls try to stay hidden. This study examines if it is possible to identify troll farms on Twitter by conducting a sentiment analysis on user tweets and modeling it as a nearest neighbor problem. The experiment was done with 4 simulated trolls and 150 normal twitter users. The users were modelled into datapoints based on the sentiment, frequen

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KUMAR, SUSMIT. "NEAREST NEIGHBOR SEARCH IN DISTRIBUTED DATABASES." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1022879916.

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Ram, Parikshit. "New paradigms for approximate nearest-neighbor search." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49112.

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Nearest-neighbor search is a very natural and universal problem in computer science. Often times, the problem size necessitates approximation. In this thesis, I present new paradigms for nearest-neighbor search (along with new algorithms and theory in these paradigms) that make nearest-neighbor search more usable and accurate. First, I consider a new notion of search error, the rank error, for an approximate neighbor candidate. Rank error corresponds to the number of possible candidates which are better than the approximate neighbor candidate. I motivate this notion of error and present new ef

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Chanzy, Philippe. "Range search and nearest neighbor search in k-d trees." Thesis, McGill University, 1993. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=68164.

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This thesis presents an analysis of the expected complexity of range searching and nearest neighbor searching in random 2-d trees. We show that range searching in a random rectangle $ Delta sb{x} times Delta sb{y}$ can be done in $O lbrack Delta sb{x} Delta sb{y} n+( Delta sb{x}+ Delta sb{y}) n sp alpha +{ rm ln} n rbrack$ expected time. A matching lower bound is also obtained. We also show that nearest neighbor searching in random 2-d trees by any algorithm must take time bounded by $ Omega lbrack n sp{ alpha-1/2}/({ rm ln} n) sp alpha rbrack$ where $ alpha=( sqrt{17}-3)/2$. This disproves a

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MESEJO-LEON, DANIEL ALEJANDRO. "APPROXIMATE NEAREST NEIGHBOR SEARCH FOR THE KULLBACK-LEIBLER DIVERGENCE." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=33305@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Em uma série de aplicações, os pontos de dados podem ser representados como distribuições de probabilidade. Por exemplo, os documentos podem ser representados como modelos de tópicos, as imagens podem ser representadas como histogramas e também a música pode ser representada como uma distribuição de probabilidade. Neste trabalho, abordamos o problema do Vizinho Próximo Aproximado onde os pontos são distribuições de probabilidade e a função de

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Varricchio, Valerio. "Efficient nearest-neighbor search algorithms for sub-Riemannian geometries." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122500.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references.<br>The Motion Planning problem has been at the core of a significant amount of research in the past decades and it has recently gained traction outside academia with the rise of commercial interest in self-driving cars and autonomous aerial vehicles. Among the leading algorithms to tackle the problem are sampling-based planners, such as Probabilistic Road Maps (PRMs), Rapidly-exploring Random Trees (RRTs) and a

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Zhang, Peiwu, and 张培武. "Voronoi-based nearest neighbor search for multi-dimensional uncertain databases." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B49618179.

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In Voronoi-based nearest neighbor search, the Voronoi cell of every point p in a database can be used to check whether p is the closest to some query point q. We extend the notion of Voronoi cells to support uncertain objects, whose attribute values are inexact. Particularly, we propose the Possible Voronoi cell (or PV-cell). A PV-cell of a multi-dimensional uncertain object o is a region R, such that for any point p ∈ R, o may be the nearest neighbor of p. If the PV-cells of all objects in a database S are known, they can be used to identify objects that have a chance to be the nearest neig

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Andoni, Alexandr. "Nearest neighbor search : the old, the new, and the impossible." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/55090.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 165-178).<br>Over the last decade, an immense amount of data has become available. From collections of photos, to genetic data, and to network traffic statistics, modern technologies and cheap storage have made it possible to accumulate huge datasets. But how can we effectively use all this data? The ever growing sizes of the datasets make it imperative to design new algorithms capable of sifting

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Books on the topic "Search for the nearest neighbour"

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Large Scale Nearest Neighbor Search - Theories, Algorithms, and Applications. [publisher not identified], 2014.

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Weber, Roger. Similarity search in high dimensional vector spaces. Aka, 2001.

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Başan, Ghillie. The moon's our nearest neighbour. Warner Books, 2001.

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Roopchansingh, Ajay. Nearest neighbour interconnect architecture in deep-submicron FPGAs. National Library of Canada, 2002.

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Gwennyth, Zainu'ddin Ailsa, Australian Indonesian Association Victoria, and Monash University. Centre of Southeast Asian Studies., eds. Nearest southern neighbour: Some Indonesian views of Australia and Australians. Monash University, 1986.

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Guo, Gongde. A study on the nearest neighbour method and its applications. The Author], 2004.

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Brandsma, Theo. Rainfall generator for the Rhine Basin: Single-site generation of weather variables by nearest-neighbour resampling. KNMI, 1997.

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Nearest Neighbor Search. Springer US, 2005. http://dx.doi.org/10.1007/0-387-27544-4.

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Papadopoulos, Apostolos N., and Yannis Manolopoulos. Nearest Neighbor Search : : A Database Perspective. Springer London, Limited, 2006.

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Manolopoulos, Yannis, and Apostolos N. N. Papadopoulos. Nearest Neighbor Search : : A Database Perspective. Springer, 2010.

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Book chapters on the topic "Search for the nearest neighbour"

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Shekhar, Shashi, and Hui Xiong. "Nearest Neighbor Search." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_867.

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Khan, Omar Shahbaz, Martin Aumüller, and Björn Þór Jónsson. "Suitability of Nearest Neighbour Indexes for Multimedia Relevance Feedback." In Similarity Search and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46994-7_12.

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Serrano, Aureo, Luisa Micó, and Jose Oncina. "Which Fast Nearest Neighbour Search Algorithm to Use?" In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_67.

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Weller, Frank, and Robert Mencl. "Nearest Neighbour Search for Visualization Using Arbitrary Triangulations." In Eurographics. Springer Vienna, 1996. http://dx.doi.org/10.1007/978-3-7091-7488-3_20.

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Chappell, Timothy, Shlomo Geva, and Guido Zuccon. "Approximate Nearest-Neighbour Search with Inverted Signature Slice Lists." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16354-3_16.

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Gómez-Ballester, Eva, Luisa Micó, and Jose Oncina. "Some Improvements in Tree Based Nearest Neighbour Search Algorithms." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24586-5_56.

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Shaneck, Mark, Yongdae Kim, and Vipin Kumar. "Privacy Preserving Nearest Neighbor Search." In Machine Learning in Cyber Trust. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-88735-7_10.

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Hruz, Tomas, and Marcel Schöngens. "Partially Specified Nearest Neighbor Search." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32241-9_32.

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Komorowski, Michał, and Tomasz Trzciński. "Random Binary Search Trees for Approximate Nearest Neighbour Search in Binary Space." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69900-4_60.

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Wu, Xing, Geoffrey Holmes, and Bernhard Pfahringer. "Mining Arbitrarily Large Datasets Using Heuristic k-Nearest Neighbour Search." In AI 2008: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89378-3_35.

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Conference papers on the topic "Search for the nearest neighbour"

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Ferro, Demetrio, Vincent Gripon, and Xiaoran Jiang. "Nearest Neighbour Search using binary neural networks." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727873.

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Dick, Travis, Camilo Perez, Martin Jagersand, and Azad Shademan. "Realtime Registration-Based Tracking via Approximate Nearest Neighbour Search." In Robotics: Science and Systems 2013. Robotics: Science and Systems Foundation, 2013. http://dx.doi.org/10.15607/rss.2013.ix.044.

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Stommel, Martin, Stefan Edelkamp, Thiemo Wiedemeyer, and Michael Beetz. "Fractal Approximate Nearest Neighbour Search in Log-Log Time." In British Machine Vision Conference 2013. British Machine Vision Association, 2013. http://dx.doi.org/10.5244/c.27.18.

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Vijay, Savinu T., and P. N. Pournami. "Feature Based Image Registration using Heuristic Nearest Neighbour Search." In 2018 22nd International Computer Science and Engineering Conference (ICSEC). IEEE, 2018. http://dx.doi.org/10.1109/icsec.2018.8712669.

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Kurniawati, R., J. S. Jin, and J. A. Shepherd. "An efficient nearest-neighbour search while varying Euclidean metrics." In the sixth ACM international conference. ACM Press, 1998. http://dx.doi.org/10.1145/290747.290812.

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Shao, Zhou, and David Taniar. "Range-based Nearest Neighbour Search in a Mobile Environment." In MoMM '14: The 12th International Conference on Advances in Mobile Computing and Multimedia. ACM, 2014. http://dx.doi.org/10.1145/2684103.2684158.

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Chatterjee, Bapi, Ivan Walulya, and Philippas Tsigas. "Concurrent Linearizable Nearest Neighbour Search in LockFree-kD-tree." In ICDCN '18: 19th International Conference on Distributed Computing and Networking. ACM, 2018. http://dx.doi.org/10.1145/3154273.3154307.

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Moreno-Seco, F., L. Mico, and J. Oncina. "A new classification rule based on nearest neighbour search." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1333789.

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Tellez, Eric Sadit, Edgar Chávez, and Gonzalo Navarro. "Succinct nearest neighbor search." In the Fourth International Conference. ACM Press, 2011. http://dx.doi.org/10.1145/1995412.1995420.

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"Grid-based Spatial Index Method for Location-based Nearest Neighbour Search." In 2019 the 9th International Workshop on Computer Science and Engineering. WCSE, 2019. http://dx.doi.org/10.18178/wcse.2019.03.030.

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Reports on the topic "Search for the nearest neighbour"

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Gonzales, Antonio, and Nicholas Paul Blazier. Enhanced Approximate Nearest Neighbor via Local Area Focused Search. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1367491.

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Mackey, Greg Edward. Efficient nearest neighbor searches in N-ABLE. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/992313.

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