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Efficient reverse k-nearest neighbor estimation
2007
Informatik - Forschung und Entwicklung
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The reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest neighbors of which include a specified query object, has received increasing attention recently. ...
In this paper, we propose the first approach for efficient approximative RkNN search in arbitrary metric spaces where the value of k is specified at query time. ...
Although the reverse k-nearest neighbor problem is the complement of the k-nearest neighbor problem, the relationship between kNN and RkNN is not symmetric and the number of the reverse k-nearest neighbors ...
doi:10.1007/s00450-007-0027-z
fatcat:iiqfjtwprvcsvixyr6arpslwwm
A Local Density-Based Approach for Local Outlier Detection
[article]
2016
arXiv
pre-print
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Instead of using only k nearest neighbors, we further consider reverse nearest neighbors and shared nearest neighbors of an object for density distribution estimation. ...
based on extended nearest neighbors of the object. ...
Three kinds of neighbors: k nearest neighbors, reverse nearest neighbors, and shared nearest neighbors, are considered in our local kernel density estimation. ...
arXiv:1606.08538v1
fatcat:ihsldcayrree7c3eejurroar3a
Unsupervised Distance-Based Outlier Detection Using Nearest Neighbours Algorithm on Distributed Approach: Survey
English
2014
International Journal of Innovative Research in Computer and Communication Engineering
English
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By examining again the notion of reverse nearest neighbors in the unsupervised outlier-detection context, high dimensionality can have a different impact. ...
In high dimensions it was observed that the distribution of points in reverse-neighbor counts becomes skewed .This proposed work aims at developing and comparing some of the unsupervised outlier detection ...
The local density for instances is computed by dividing volume of k,i.e k nearest neighbor and volume of hyper sphere. ...
doi:10.15680/ijircce.2014.0212042
fatcat:7okbstpahnbctltcf65f6cjmfi
Ranking Outliers Using Symmetric Neighborhood Relationship
[chapter]
2006
Lecture Notes in Computer Science
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The estimation of density distribution at the location of an object has so far been based on the density distribution of its k-nearest neighbors [2, 11] . ...
The proposed measure considers both neighbors and reverse neighbors of an object when estimating its density distribution. As a result, outliers so discovered are more meaningful. ...
To get a better estimation of the neighborhood's density distribution, we propose to take both the nearest neighbors (NNs) and reverse nearest neighbors (RNNs) [14] into account. ...
doi:10.1007/11731139_68
fatcat:5zarj2sf3fefzcsgcgqxn5bqr4
Efficient reverse k-nearest neighbor search in arbitrary metric spaces
2006
Proceedings of the 2006 ACM SIGMOD international conference on Management of data - SIGMOD '06
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The reverse k-nearest neighbor (RkNN) problem, i.e. finding all objects in a data set the k-nearest neighbors of which include a specified query object, is a generalization of the reverse 1-nearest neighbor ...
In particular, we approximate the k-nearest neighbor distance for each data object by upper and lower bounds using two functions of only two parameters each. ...
Acknowledgement We are grateful with Yufei Tao, Dimitris Papadias, Xiang Lian and Xiaokui Xiao providing us the source code of their Reverse kNN Search in [11] . ...
doi:10.1145/1142473.1142531
dblp:conf/sigmod/AchtertBKKPR06
fatcat:3qgpvzngvrbf3gxvofqb26vxge
BORDER: efficient computation of boundary points
2006
IEEE Transactions on Knowledge and Data Engineering
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BORDER employs the state-of-the-art database technique -the Gorder kNN join and makes use of the special property of the reverse k-nearest neighbor (RkNN) . ...
Our experimental study shows that BORDER detects boundary points effectively and efficiently on various datasets. ...
The reverse k-nearest neighbors (RkNN) of an object p are points that look upon p as one of their k-nearest neighbors. ...
doi:10.1109/tkde.2006.38
fatcat:iqdkqmambzbztmb52zgexlwfhm
Fast and Scalable Outlier Detection with Approximate Nearest Neighbor Ensembles
[chapter]
2015
Lecture Notes in Computer Science
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Popular outlier detection methods require the pairwise comparison of objects to compute the nearest neighbors. ...
neighbors. ...
If k-nearest neighborhoods are needed, as it is the case for the most wellknown outlier detection methods, SFC is the method of choice. ...
doi:10.1007/978-3-319-18123-3_2
fatcat:jbhn3t6vhrbqdcwwnjdzhaz3zu
Efficient clustering method based on density peaks with symmetric neighborhood relationship
2019
IEEE Access
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In this paper, we propose a robust clustering method which establishes a symmetric neighborhood graph over all data points, based on the k-nearest neighbors and reverse k-nearest neighbors of each point ...
INDEX TERMS Clustering, symmetric neighborhood, reverse k-nearest neighbors, density peaks clustering. 60684 2169-3536 ...
Our new method establishes a symmetric neighborhood graph over all data points, which is achieved using the k-nearest neighbors and reverse k-nearest neighbors of each point. ...
doi:10.1109/access.2019.2912332
fatcat:qj3r234dkjbbjoip3xyx475rk4
Approximate Direct and Reverse Nearest Neighbor Queries, and the k-nearest Neighbor Graph
2009
2009 Second International Workshop on Similarity Search and Applications
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A related, far less explored primitive is to obtain the dataset elements which would have the query object within their own k-nearest neighbors, known as the reverse k-nearest neighbor query. ...
The naive algorithm for reverse k-nearest neighbor queries has quadratic complexity, because the k-nearest neighbors of all the dataset objects must be found; this is too expensive. ...
k-nearest neighbors. ...
doi:10.1109/sisap.2009.33
dblp:conf/sisap/FigueroaP09
fatcat:wieiyzyfdngdlarrn5rg2bjv6m
KNN-FSVM for Fault Detection in High-Speed Trains
2018
2018 IEEE International Conference on Prognostics and Health Management (ICPHM)
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In this paper, the reverse nearest neighbors technique is adopted for detecting outliers and the k nearest neighbors technique is used to identify the borderline points for defining the classification ...
Considering the position of each data point and the distribution of its nearest neighbors, a new method is proposed for calculating their estimation error costs. ...
A k Nearest Neighbors-based Fuzzy Support Vector Machine (KNN-FSVM) is proposed in this paper. The reverse nearest neighbor method is used to detect and eliminate the noise and outliers [33] . ...
doi:10.1109/icphm.2018.8448688
dblp:conf/icphm/LiuZ18
fatcat:3oq22dednbe67d2xjs7d5lloie
Dimensional testing for reverse k-nearest neighbor search
2017
Proceedings of the VLDB Endowment
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Given a query object q, reverse k-nearest neighbor (RkNN) search aims to locate those objects of the database that have q among their k-nearest neighbors. ...
The method can accommodate any index structure supporting incremental (forward) nearest-neighbor search for the generation and verification of candidates, while avoiding impractically-high preprocessing ...
Accordingly, x cannot be a reverse k-nearest neighbor of q. ...
doi:10.14778/3067421.3067426
fatcat:wx4dp2k2x5h7tnifkqichxdomy
A Voronoi-Based Group Reverse k Farthest Neighbor Query Method in the Obstacle Space
2020
IEEE Access
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Then we get the candidate set using the theorem of transformation between k nearest neighbors and k farthest neighbors. Finally, the refining algorithm is given to get the final results. ...
To this end, this paper proposed the V-OGRkFN(Voronoi-Obstacle Group Reverse k Farthest Neighbor) algorithm. ...
A new density-based clustering algorithm, RNN-DBSCAN, was presented which uses reverse nearest neighbor counts as an estimate of observation density by Pei et al. [34] . ...
doi:10.1109/access.2020.2979739
fatcat:ovfyvjnltvhldjoz2rl44nsacq
Nearest neighbor influences on DNA polymerase insertion fidelity
1989
Journal of Biological Chemistry
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Reverse transcriptase forms transversions with lower efficiencies than transitions, especially low being A.G, G.G, and C.C. ...
Conversely, target sites with nearest neighbor purines have a higher than average Vmax component. ...
The one exception to the trend is the case of nearest neighbor T where Evidence of a 6'-nearest neighbor influence on the kinetic components of misinsertion efficiency. ...
pmid:2474545
fatcat:7jo2loycjjb5npce5a5wcpd4si
Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques
2019
Applied Sciences
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These results show the superiority of the presented RF model in estimation of early heating load in energy-efficient buildings. ...
regressor (MLPr), lazy locally weighted learning (LLWL), alternating model tree (AMT), random forest (RF), ElasticNet (ENet), and radial basis function regression (RBFr) for the problem of designing energy-efficient ...
Figure 6 . 6 Example of k-nearest neighbors (KNN) regression/classification. ...
doi:10.3390/app9204338
fatcat:6okjwi75vncl5i4mnxzny7qe4e
Ranking with Distance based Outlier Detection Techniques: A Survey
2014
International Journal of Computer Applications
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The proposed measure considers both neighbors and reverse neighbors of an object when estimating its density distribution and then based on it detect top-n outliers. Carlos H. C. ...
Centrally located point in a cluster has relatively low cumulative sum of ranks because it is among the nearest neighbor of its own nearest neighbors. So, rank measures an object's outlines. ...
doi:10.5120/15505-4207
fatcat:kacyax7u7zhnnjluqmvxfgs2l4
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