Nearest Neighbor Search in General Metric Spaces Using a Tree Data Structure with a Simple Heuristic

2003 ◽  
Vol 43 (6) ◽  
pp. 1933-1941 ◽  
Author(s):  
Huafeng Xu ◽  
Dimitris K. Agrafiotis
Keyword(s):  
Data Structure ◽  
Metric Spaces ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Neighbor Search ◽  
Simple Heuristic ◽  
Tree Data ◽  
Tree Data Structure

Nearest neighbor search in metric spaces through Content-Addressable Networks

2008 ◽  
Vol 44 (1) ◽  
pp. 411-429 ◽  
Author(s):  
Fabrizio Falchi ◽  
Claudio Gennaro ◽  
Pavel Zezula
Keyword(s):  
Metric Spaces ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Neighbor Search

Antipole tree indexing to support range search and k-nearest neighbor search in metric spaces

2005 ◽  
Vol 17 (4) ◽  
pp. 535-550 ◽  
Author(s):  
D. Cantone ◽  
A. Ferro ◽  
A. Pulvirenti ◽  
D.R. Recupero ◽  
D. Shasha
Keyword(s):  
Metric Spaces ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
K Nearest Neighbor ◽  
Range Search ◽  
Neighbor Search ◽  
K Nearest Neighbor Search

Nearest neighbor search in metric spaces through Content-Addressable Networks

2007 ◽  
Vol 43 (3) ◽  
pp. 665-683 ◽  
Author(s):  
Fabrizio Falchi ◽  
Claudio Gennaro ◽  
Pavel Zezula
Keyword(s):  
Metric Spaces ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Neighbor Search

scholarly journals Reverse Nearest Neighbor Search in Metric Spaces

2006 ◽  
Vol 18 (9) ◽  
pp. 1239-1252 ◽  
Author(s):  
Yufei Tao ◽  
Man Lung Yiu ◽  
N. Mamoulis
Keyword(s):  
Metric Spaces ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Reverse Nearest Neighbor ◽  
Neighbor Search

scholarly journals Tree-Based Algorithm for Stable and Efficient Data Clustering

Informatics ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. 38
Author(s):  
Hasan Aljabbouli ◽  
Abdullah Albizri ◽  
Antoine Harfouche
Keyword(s):  
Data Structure ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Convergence Properties ◽  
Insertion Technique ◽  
Efficient Data ◽  
Tree Data ◽  
Tree Data Structure ◽  
Nearest Neighbor Searches

The K-means algorithm is a well-known and widely used clustering algorithm due to its simplicity and convergence properties. However, one of the drawbacks of the algorithm is its instability. This paper presents improvements to the K-means algorithm using a K-dimensional tree (Kd-tree) data structure. The proposed Kd-tree is utilized as a data structure to enhance the choice of initial centers of the clusters and to reduce the number of the nearest neighbor searches required by the algorithm. The developed framework also includes an efficient center insertion technique leading to an incremental operation that overcomes the instability problem of the K-means algorithm. The results of the proposed algorithm were compared with those obtained from the K-means algorithm, K-medoids, and K-means++ in an experiment using six different datasets. The results demonstrated that the proposed algorithm provides superior and more stable clustering solutions.

scholarly journals Secure and Practical Group Nearest Neighbor Query for Location-Based Services in Cloud Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jingjing Guo ◽  
Jiacong Sun
Keyword(s):  
Data Structure ◽  
Data Privacy ◽  
High Efficiency ◽  
Nearest Neighbor ◽  
Resource Constraints ◽  
Location Based Services ◽  
Nearest Neighbor Query ◽  
Tree Data ◽  
Random Database ◽  
Tree Data Structure

Group nearest neighbor (GNN) query enables a group of location-based service (LBS) users to retrieve a point from point of interests (POIs) with the minimum aggregate distance to them. For resource constraints and privacy concerns, LBS provider outsources the encrypted POIs to a powerful cloud server. The encryption-and-outsourcing mechanism brings a challenge for the data utilization. However, as previous work from k − anonymity technique leaks all contents of POIs and returns an answer set with redundant communication cost, the LBS system cannot work properly with those privacy-preserving schemes. In this paper, we illustrate a secure group nearest neighbor query scheme, which is referred to as SecGNN. It supports the GNN query with n n ≥ 3 LBS users and assures the data privacy and query privacy. Since SecGNN only achieves linear search complexity, an efficiency enhanced scheme (named Sec GNN + ) is introduced by taking advantage of the KD-tree data structure. Specifically, we convert the GNN problem to the nearest neighbor problem for their centroid, which can be computed by anonymous veto network and Burmester–Desmedt conference key agreement protocols. Furthermore, the Sec GNN + scheme is introduced from the KD-tree data structure and a designed tool, which supports the computation of inner products over ciphertexts. Finally, we run experiments on a real-database and a random database to evaluate the performance of our SecGNN and Sec GNN + schemes. The experimental results show the high efficiency of our proposed schemes.

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