kNNVWC: An Efficient k-Nearest Neighbours Approach based on Various-Widths Clustering

被引：0

作者：

Almalawi, Abdulmohsen ^{[1
]}

Fahad, Adil ^{[2
]}

Tari, Zahir ^{[3
]}

Cheema, Muhammad Aamir ^{[4
]}

Khalil, Ibrahim ^{[3
]}

机构：

[1] King Abdulaziz Univ, Sch Comp Sci & IT, Jeddah, Saudi Arabia

[2] Al Baha Univ, Coll Comp Sci & IT, Al Baha, Saudi Arabia

[3] RMIT Univ, Sch Comp Sci & IT, Melbourne, Vic, Australia

[4] Monash Univ, Fac Informat Technol, Clayton, Vic 3168, Australia

来源：

2016 32ND IEEE INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE) | 2016年

关键词：

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a novel k-NN approach based on Various-Widths Clustering, named kNNVWC, is proposed to efficiently find k-NNs for a query object from a given data set. kNNVWC does clustering using various widths, where a data set is clustered with a global width first and each produced cluster that meets the predefined criteria is recursively clustered with its own local width that suits its distribution. Experimental results demonstrate that kNNVWC performs well compared to state-of-art of k-NN search algorithms.

引用

页码：1572 / 1573

页数：2

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