scholarly journals Testing Some Improvements of the Fukunaga and Narendra’s Fast Nearest Neighbour Search Algorithm in a Spelling Task

2005 ◽  
pp. 3-10
Author(s):  
Eva Gómez-Ballester ◽  
Luisa Micó ◽  
Jose Oncina
Keyword(s):  
Search Algorithm ◽  
Nearest Neighbour

Fast K-nearest-neighbour search algorithm for nonparametric classification

2000 ◽  
Vol 36 (21) ◽  
pp. 1821 ◽  
Author(s):  
SeongJoon Baek ◽  
Koeng-Mo Sung
Keyword(s):  
Search Algorithm ◽  
Nearest Neighbour ◽  
Nonparametric Classification

PREDIKSI HARGA CRYPTOCURRENCY DENGAN METODE K-NEAREST NEIGHBOURS

2018 ◽  
Vol 14 (2) ◽  
pp. 137
Author(s):  
Haerul Fatah ◽  
Agus Subekti
Keyword(s):  
Mean Squared Error ◽  
Mean Absolute Error ◽  
Search Algorithm ◽  
Absolute Error ◽  
Nearest Neighbour ◽  
Root Mean Squared Error ◽  
Squared Error ◽  
Nearest Neighbours

Uang elektronik menjadi pilihan yang mulai ramai digunakan oleh banyak orang, terutama para pengusaha, pebisnis dan investor, karena menganggap bahwa uang elektronik akan menggantikan uang fisik dimasa depan. Cryptocurrency muncul sebagai jawaban atas kendala uang eletronik yang sangat bergantung kepada pihak ketiga. Salah satu jenis Cryptocurrency yaitu Bitcoin. Analogi keuangan Bitcoin sama dengan analogi pasar saham, yakni fluktuasi harga tidak tentu setiap detik. Tujuan dari penelitian yang dilakukan yaitu melakukan prediksi harga Cryptocurrency dengan menggunakan metode KNN (K-Nearest Neighbours). Hasil dari penelitian ini diketahui bahwa model KNN yang paling baik dalam memprediksi harga Cryptocurrency adalah KNN dengan parameter nilai K=3 dan Nearest Neighbour Search Algorithm : Linear NN Search. Dengan nilai Mean Absolute Error (MAE) sebesar 0.0018 dan Root Mean Squared Error (RMSE) sebesar 0.0089.

scholarly journals Testing Performance (Time Analysis) of Nearest Neighbour (NN) Search Algorithms on K-d Trees

2021 ◽  
Vol 10 (5) ◽  
pp. 2954-2957
Keyword(s):  
Data Structure ◽  
Search Algorithm ◽  
Dimensional Space ◽  
Binary Search Tree ◽  
Nearest Neighbour ◽  
Space Partitioning ◽  
Multidimensional Search ◽  
Data Points ◽  
Knn Search ◽  
Better Than

K-d tree (k-dimensional tree) is a space partitioning data structure for organizing points in a k-dimensional space. K-d tree, or Multidimensional Binary Search Tree is a useful data structure for several applications such as searches involving a multidimensional search key (e.g., Range Search and Nearest Neighbour Search). K-d trees are a special case of binary space partitioning trees.KNN Search is a searching algorithm with complexity O(N log N) {N= no. of data points}. This search algorithm is relatively better than brute force search {Complexity= O(n*k); where k=No. of neighbours searched, N=No. of Data Points in Kd tree} for dimensions N>>2D {N=No. of Points, D=Dimensionality of Tree}.Furthermore, Parallel KNN Search is much more efficient and performs better than KNN Search, as it harnesses parallel processing capabilities of computers and thus, results in better search time.This paper tests the time performance of KNN Search and Parallel KNN Search and compares them by plotting it on a 3D graph. A more comprehensive comparison is done by use of 2D graphs for each dimension(from 2 to 20).

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