NNB: An efficient nearest neighbor search method for hierarchical clustering on large datasets

2015 ◽  
Author(s):  
Wei Zhang ◽  
Gongxuan Zhang ◽  
Yongli Wang ◽  
Zhaomeng Zhu ◽  
Tao Li
Keyword(s):  
Hierarchical Clustering ◽  
Nearest Neighbor ◽  
Large Datasets ◽  
Search Method ◽  
Nearest Neighbor Search ◽  
Neighbor Search

NBC: An Efficient Hierarchical Clustering Algorithm for Large Datasets

2015 ◽  
Vol 09 (03) ◽  
pp. 307-331 ◽  
Author(s):  
Wei Zhang ◽  
Gongxuan Zhang ◽  
Yongli Wang ◽  
Zhaomeng Zhu ◽  
Tao Li
Keyword(s):  
Hierarchical Clustering ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Large Datasets ◽  
Nearest Neighbor Search ◽  
Large Dataset ◽  
Neighbor Search ◽  
Hierarchical Clustering Algorithm

Nearest neighbor search is a key technique used in hierarchical clustering and its computing complexity decides the performance of the hierarchical clustering algorithm. The time complexity of standard agglomerative hierarchical clustering is O(n3), while the time complexity of more advanced hierarchical clustering algorithms (such as nearest neighbor chain, SLINK and CLINK) is O(n2). This paper presents a new nearest neighbor search method called nearest neighbor boundary (NNB), which first divides a large dataset into independent subset and then finds nearest neighbor of each point in subset. When NNB is used, the time complexity of hierarchical clustering can be reduced to O(n log 2n). Based on NNB, we propose a fast hierarchical clustering algorithm called nearest-neighbor boundary clustering (NBC), and the proposed algorithm can be adapted to the parallel and distributed computing framework. The experimental results demonstrate that our algorithm is practical for large datasets.

scholarly journals A Radial Search Method for Fast Nearest Neighbor Search on Range Images

2016 ◽  
pp. 563-577
Author(s):  
Federico Tombari ◽  
Samuele Salti ◽  
Luca Puglia ◽  
Giancarlo Raiconi ◽  
Luigi Di Stefano
Keyword(s):  
Nearest Neighbor ◽  
Search Method ◽  
Nearest Neighbor Search ◽  
Range Images ◽  
Neighbor Search

Acceleration of Fractal Image Compression by Fast Nearest-Neighbor Search

Fractals ◽  
1997 ◽  
Vol 05 (supp01) ◽  
pp. 231-241
Author(s):  
Leszek Ciepliński ◽  
Czesław Jȩdrzejek ◽  
Tomasz Major
Keyword(s):  
Image Compression ◽  
Nearest Neighbor ◽  
Search Method ◽  
Nearest Neighbor Search ◽  
Block Matching ◽  
Fractal Image Compression ◽  
Neighbor Search ◽  
Fractal Image ◽  
Partial Distortion ◽  
The Impact

In this paper we investigate the effect of fast nearest neighbor search method on acceleration of fractal image compression. First we follow the Saupe1 encoding step of fractal image compression that uses the multi-dimensional nearest neighbor search in a projected space. Then we investigate performance of a method for finding the nearest vector called partial distortion elimination. We also propose some supplementary accelerating concepts. For all inspected methods the impact of the tolerance criterion for mean square error of block matching is examined.

Sign in / Sign up

Export Citation Format

Share Document