Efficient kNN classification algorithm for big data

K nearest neighbors (kNN) is an efficient lazy learning algorithm and has successfully been developed in real applications. It is natural to scale the kNN method to the large scale datasets. In this paper, we propose to first conduct a k-means clustering to separate the whole dataset into several pa...

Full description

Saved in:
Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 195; pp. 143 - 148
Main Authors Deng, Zhenyun, Zhu, Xiaoshu, Cheng, Debo, Zong, Ming, Zhang, Shichao
Format Journal Article
LanguageEnglish
Published Elsevier B.V 26.06.2016
Subjects
Algorithms
Big data
Classification
Cluster analysis
Cluster center
Data cluster
Data management
K nearest neighbour classification tree analysis
kNN
Learning
Medical imaging
Vector quantization
Data cluster
kNN
Big data
Cluster center
Classification
Online AccessGet full text
ISSN0925-2312
1872-8286
DOI10.1016/j.neucom.2015.08.112

Cover

More Information
Summary:K nearest neighbors (kNN) is an efficient lazy learning algorithm and has successfully been developed in real applications. It is natural to scale the kNN method to the large scale datasets. In this paper, we propose to first conduct a k-means clustering to separate the whole dataset into several parts, each of which is then conducted kNN classification. We conduct sets of experiments on big data and medical imaging data. The experimental results show that the proposed kNN classification works well in terms of accuracy and efficiency.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2015.08.112