k — Nearest — Neighbor Voronoi diagrams for sets of convex polygons, line segments and points

1990 ◽  
pp. 330-340 ◽  
Author(s):  
Thomas Roos
Keyword(s):  
Nearest Neighbor ◽  
Voronoi Diagrams ◽  
K Nearest Neighbor ◽  
Convex Polygons ◽  
Line Segments

Processing Continuous k Nearest Neighbor Queries in Obstructed Space with Voronoi Diagrams

2020 ◽  
Vol 7 (2) ◽  
pp. 1-27
Author(s):  
Huaijie Zhu ◽  
Xiaochun Yang ◽  
Bin Wang ◽  
Wang-Chien Lee ◽  
Jian Yin ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Voronoi Diagrams ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Queries

Line Segment Nearest Neighbor Query of Spatial Database Based on R*S-Tree

2011 ◽  
Vol 201-203 ◽  
pp. 194-197
Author(s):  
Dian Zhu Sun ◽  
Yong Wei Sun ◽  
Xin Cai Kang ◽  
Yan Rui Li
Keyword(s):  
Line Segment ◽  
Nearest Neighbor ◽  
Target Line ◽  
Data Type ◽  
Spatial Indexing ◽  
K Nearest Neighbor ◽  
Line Segments ◽  
Nearest Neighbor Query ◽  
Indexing Structure ◽  
Hollow Ball

An algorithm for nearest neighbor query of Line Segment based on the R*S-tree is proposed. The dynamic spatial indexing structure for spatial line segments was constructed based on the R*S-tree, and the k-nearest neighbor of the target line segment were obtained by the hollow ball. The distance between the target line segment and the neighbor line segments was computed, and the neighbor line segments were sorted by the distance. The result shows that the algorithm can obtain nearest neighbor line segment accurately and effectively and has the strong adaptability of data type.

scholarly journals Computational Geometry Computation and KNN Segmentation in ITK

2006 ◽  
Author(s):  
Ruben Cardenes ◽  
Manuel Rene Sanchez ◽  
Juan Ruiz-Alzola
Keyword(s):  
Computational Geometry ◽  
Nearest Neighbor ◽  
Brain Mri ◽  
Voronoi Diagrams ◽  
K Nearest Neighbor ◽  
Fast Marching ◽  
Image Objects ◽  
Supervised Segmentation ◽  
And Performance ◽  
2D And 3D

This work describes the implementation of computational geometry algorithms developed within the Insight Toolkit (ITK): Distance Transform (DT), Voronoi diagrams, k Nearest Neighbor (kNN) transform, and finally a K Nearest Neighbor classifier for multichannel data, that is used for supervised segmentation. We have tested this algorithm for 2D and 3D medical datasets, and the results are excellent in terms of accuracy and performance. One of the strongest points of the algorithms described here is that they can be used for many other applications, because they are based on the ordered propagation paradigm. This idea consists in actually not raster scan the image but rather in start from the image objects and propagate them until the image is totally filled. This has been demonstrated to be a good approach in many algorithms as for example, computation of Distance Transforms, Voronoi Diagrams, Fast Marching, skeletons computation, etc. We show here that these algorithms have low computational complexity and it provides excellent results for clinical applications as the segmentation of brain MRI.

A SIMPLE ON-LINE RANDOMIZED INCREMENTAL ALGORITHM FOR COMPUTING HIGHER ORDER VORONOI DIAGRAMS

1992 ◽  
Vol 02 (04) ◽  
pp. 363-381 ◽  
Author(s):  
FRANZ AURENHAMMER ◽  
OTFRIED SCHWARZKOPF
Keyword(s):  
Voronoi Diagram ◽  
Structural Changes ◽  
Nearest Neighbor ◽  
Voronoi Diagrams ◽  
Simple Algorithm ◽  
Incremental Algorithm ◽  
K Nearest Neighbor ◽  
On Line ◽  
Incremental Construction ◽  
Primal Space

We present a simple algorithm for maintaining order-k Voronoi diagrams in the plane. By using a duality transform that is of interest in its own right, we show that the insertion or deletion of a site involves little more than the construction of a single convex hull in three-space. In particular, the order-k Voronoi diagram for n sites can be computed in time [Formula: see text] and optimal space [Formula: see text] by an on-line randomized incremental algorithm. The time bound can be improved by a logarithmic factor without losing much simplicity. For k≥ log 2 n, this is optimal for a randomized incremental construction; we show that the expected number of structural changes during the construction is ⊝(nk2). Finally, by going back to primal space, we obtain a dynamic data structure that supports k-nearest neighbor queries, insertions, and deletions in a planar set of sites. The structure promises easy implementation, exhibits a satisfactory expected performance, and occupies no more storage than the current order-k Voronoi diagram.

Machine Learning Verdict of EEG Signals in Brain Computer Interface

2018 ◽  
pp. 429-441
Author(s):  
M. Jeyanthi ◽  
C. Velayutham
Keyword(s):  
Nearest Neighbor ◽  
Technology Development ◽  
Vital Role ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Data Mining Technique ◽  
Data Set ◽  
Eeg Data ◽  
Irrelevant Attributes

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.

PENENTUAN DAERAH PRIORITAS PELAYANAN AKTA KELAHIRAN DENGAN METODE K-NN DAN K-MEANS

2020 ◽  
Vol 17 (1) ◽  
pp. 319-328
Author(s):  
Ade Muchlis Maulana Anwar ◽  
Prihastuti Harsani ◽  
Aries Maesya
Keyword(s):  
Nearest Neighbor ◽  
Information Gain ◽  
Birth Certificate ◽  
Population Data ◽  
Community Services ◽  
Birth Certificates ◽  
Similar Data ◽  
K Nearest Neighbor ◽  
Civil Registration ◽  
The Family

Population Data is individual data or aggregate data that is structured as a result of Population Registration and Civil Registration activities. Birth Certificate is a Civil Registration Deed as a result of recording the birth event of a baby whose birth is reported to be registered on the Family Card and given a Population Identification Number (NIK) as a basis for obtaining other community services. From the total number of integrated birth certificate reporting for the 2018 Population Administration Information System (SIAK) totaling 570,637 there were 503,946 reported late and only 66,691 were reported publicly. Clustering is a method used to classify data that is similar to others in one group or similar data to other groups. K-Nearest Neighbor is a method for classifying objects based on learning data that is the closest distance to the test data. k-means is a method used to divide a number of objects into groups based on existing categories by looking at the midpoint. In data mining preprocesses, data is cleaned by filling in the blank data with the most dominating data, and selecting attributes using the information gain method. Based on the k-nearest neighbor method to predict delays in reporting and the k-means method to classify priority areas of service with 10,000 birth certificate data on birth certificates in 2019 that have good enough performance to produce predictions with an accuracy of 74.00% and with K = 2 on k-means produces a index davies bouldin of 1,179.

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