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Combining Elimination Rules in Tree-Based Nearest Neighbor Search Algorithms
[chapter]
2010
Lecture Notes in Computer Science
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In this paper, a new algorithm based on the combination of different pruning rules is proposed. ...
To reduce the computational overhead when the naive exhaustive search is applied, some fast nearest neighbor search (NNS) algorithms have appeared in the last years. ...
Conclusions and Further Works A new algorithm has been defined to optimize the combination of several pruning rules using the FNA tree-based search algorithm. ...
doi:10.1007/978-3-642-14980-1_7
fatcat:p7fn3weutrghxdoaecyfwpkoe4
PCA-based branch and bound search algorithms for computing K nearest neighbors
2003
Pattern Recognition Letters
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Then, different elimination rules and traversal orders are combined resulting in ten different search algorithms. ...
In this paper, the efficiency of branch and bound search algorithms for the computation of K nearest neighbors is studied. ...
Acknowledgements This work was financially supported by the Flemish Institute for the Promotion of Scientific and Technological Research in the Industry (IWT), Brussels. ...
doi:10.1016/s0167-8655(02)00384-7
fatcat:mxyxe7dgavavzhuahhop7yn4pq
Fast nearest-neighbor search algorithms based on approximation-elimination search
2000
Pattern Recognition
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In this paper, we provide an overview of fast nearest-neighbor search algorithms based on an &approxima-tion}elimination' framework under a class of elimination rules, namely, partial distance elimination ...
Previous algorithms based on these elimination rules are reviewed in the context of approximation}elimination search. ...
Triangle-inequality-based fast search Another important elimination rule which has been used extensively for fast nearest-neighbor search is the triangle inequality-based elimination, applicable when the ...
doi:10.1016/s0031-3203(99)00134-x
fatcat:262obja7szdqvkq7c5ooybz6yy
On the Use of Different Classification Rules in an Editing Task
[chapter]
2006
Lecture Notes in Computer Science
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This algorithm consists on the elimination of prototypes in the training set that are misclassied using the k-NN rule. ...
In this paper, we analyze the behavior of this general editing procedure combined with 3 dierent neighborhood-based classication rules, including k-NN. ...
Acknowledgments This work has been supported in part by grant TIC200308496 from the Spanish CICYT (Ministerio de Ciencia y Tecnología), GV06/166 from Generalitat Valenciana, and the IST Programme of the ...
doi:10.1007/11815921_82
fatcat:uzousdqeovc5xfxdcpwrmxqm5q
CONNEKT: Co-Located Nearest Neighbor Search using KNN Querying with K-D Tree
2019
International journal of recent technology and engineering
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For the above-said purpose, co-located nearest neighbor search algorithm namely "CONNEKT" is proposed. ...
Hence the main aim of this work is to extend the K-Nearest Neighbor (KNN) querying to co-located instances for context aware based querying or location-based services (LBS). ...
A partially specified nearest neighbor query search is used in [28] . The algorithm is based upon the k-d tree algorithms for partial match searches and nearest neighbor searches. ...
doi:10.35940/ijrte.b1741.078219
fatcat:ebqamcboxvdijlja5t5lutz5xi
Efficient Spatial Nearest Neighbor Queries Based on Multi-layer Voronoi Diagrams
[article]
2019
arXiv
pre-print
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Nearest neighbor (NN) problem is an important scientific problem. ...
In the experiments, we evaluate the efficiency of this indexing for both NN search and kNN search by comparing with VoR-tree, R-tree and kd-tree. ...
Therefore, the VoR-tree utilizes the tree structure to achieve the nearest neighbor search, and then realize a series of the other nearest neighbor related search through the pointwise nearest neighbor ...
arXiv:1911.02788v1
fatcat:pxv6wnj7gzgd3cgrddv4w5b3ei
An Efficient Indexing Scheme Based on Linked-Node m-Ary Tree Structure
[chapter]
2013
Lecture Notes in Computer Science
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In this work, we propose a linked-node m-ary tree (LM-tree) algorithm, which works really well for both exact and approximate nearest neighbor search. ...
Fast nearest neighbor search is a crucial need for many recognition systems. ...
Exact Nearest Neighbor Search in the LM-Tree Exact nearest neighbor search in the LM-tree is proceeded using a branch-andbound algorithm. ...
doi:10.1007/978-3-642-41181-6_76
fatcat:cmqfktu2gfefjpauxrzf655osm
Ground and Non-Ground Filtering for Airborne LIDAR Data
2016
International Journal of Advanced Remote Sensing and GIS
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K-D tree is used to distinguish the bare ground and nonground objects using nearest neighbor search. Experimental results show the effectiveness of the proposed approach. ...
The proposed approach in this paper is based on neighborhood based approach. Hierarchy of preprocessing is done for LIDAR data using various essential tools. ...
Nearest Neighbor Search The nearest neighbor search formula aims to seek out purpose within the tree that's nearest to a given input point. 1) Starting with the foundation node the formula moves down the ...
doi:10.23953/cloud.ijarsg.41
fatcat:cijfvkcs5ncaff6omr57fgolnq
AVR-Tree: Speeding Up the NN and ANN Queries on Location Data
[chapter]
2013
Lecture Notes in Computer Science
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In the paper, we study the problems of nearest neighbor queries (NN) and all nearest neighbor queries (ANN) on location data, which have a wide range of applications such as Geographic Information System ...
We also conduct a comprehensive performance evaluation for the proposed techniques based on both real and synthetic data, which shows that AVR-Tree based NN and ANN algorithms achieve better performance ...
Evaluate Nearest Neighbor Search In the first set of experiments, we evaluate the performance of three NN search algorithms based on R-tree, VOR-Tree and AVR-Tree against dataset LB , CA and USA, where ...
doi:10.1007/978-3-642-37487-6_11
fatcat:faopnvdcbvf3fgsggl4h54xbha
A SURVEY ON OPTIMAL ROUTE QUERIES FOR ROAD NETWORKS
2013
International Journal of Research in Engineering and Technology
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Internet based maps are now widely used for this purpose. Route search and optimal route queries are two important classes of queries based on road network concept. ...
In daily life the need to find optimal routes between two points is critical, for example finding the shortest distance to the nearest hospital. ...
Then this forward search algorithm will use the backward search algorithm for the backtracking process. This will eliminate the demerits of the greedy algorithm. ...
doi:10.15623/ijret.2013.0212075
fatcat:i3mjjd6k4rfhnivqv6oozzrx7q
Anytime k-nearest neighbor search for database applications
2008
2008 IEEE 24th International Conference on Data Engineering Workshop
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In support of these applications, we explored a search strategy for knearest neighbor searches with MVP-trees that greedily identifies k answers and then improves the answer set monotonically. ...
Given unbounded time the algorithm terminates with an exact solution. Approximate solutions to k-nearest neighbor search provide much needed speed improvement to hard nearest-neighbor problems. ...
This research is funded in part by the National Science Foundation grant DBI-0640923.
References ...
doi:10.1109/icdew.2008.4498354
dblp:conf/icde/XuMMR08
fatcat:vppvtvfoinghre3nfmx6phqyjq
Anytime K-Nearest Neighbor Search for Database Applications
2008
First International Workshop on Similarity Search and Applications (sisap 2008)
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In support of these applications, we explored a search strategy for knearest neighbor searches with MVP-trees that greedily identifies k answers and then improves the answer set monotonically. ...
Given unbounded time the algorithm terminates with an exact solution. Approximate solutions to k-nearest neighbor search provide much needed speed improvement to hard nearest-neighbor problems. ...
This research is funded in part by the National Science Foundation grant DBI-0640923.
References ...
doi:10.1109/sisap.2008.11
dblp:conf/sisap/XuMMR08
fatcat:nxr4l7iv7bbupgj5gw6olk6xyu
Regression on feature projections
2000
Knowledge-Based Systems
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The ®rst averaging process is to ®nd the individual predictions of features by using the K-Nearest Neighbor (KNN) algorithm. The second averaging process combines the predictions of all features. ...
We have compared RFP with KNN and the rule based-regression algorithms. ...
As the instances are sorted according to feature values in the training phase, the nearest neighbors can be found using a binary search. ...
doi:10.1016/s0950-7051(00)00060-5
fatcat:xjqhweyqdvgy3agiehzv67u4ca
Regression by Feature Projections
[chapter]
1999
Lecture Notes in Computer Science
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The ®rst averaging process is to ®nd the individual predictions of features by using the K-Nearest Neighbor (KNN) algorithm. The second averaging process combines the predictions of all features. ...
We have compared RFP with KNN and the rule based-regression algorithms. ...
As the instances are sorted according to feature values in the training phase, the nearest neighbors can be found using a binary search. ...
doi:10.1007/978-3-540-48247-5_75
fatcat:eg6s6dy3nrbkvgmpox2d5iudbi
Pruning Classification Rules with Instance Reduction Methods
2015
International Journal of Machine Learning and Computing
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The search strategies used by the three algorithms vary in terms of both type (depth-first or beam search) and direction (general-to-specific or specific-to-general). ...
Generating classification rules from data often leads to large sets of rules that need to be pruned. ...
RISE (Rule Induction from Set of Examples) [9] tries to combine the best characteristics of rule induction and instance based learning [10] in a single algorithm. ...
doi:10.7763/ijmlc.2015.v5.505
fatcat:znfxqnc6vbdl3p3uk22dtw4axi
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