The nearest neighbor algorithm of filling missing data based on cluster analysis

DOI

10.2991/iccsee.2013.643How to use a DOI?

Keywords

Grey mcorrelation, Mahalanobis distance, Cluster analysis, Nearest neighbor alorithm, Maximum

Abstract

Missing data universally exists in various research fields and it results in bad computational performance and effcet. In order to improve the accuracy of filling in the missing data, a filling missing data algorithm of the nearest neighbor based on the cluster analysis is proposed by this paper. After clustering data analysis,the algorithm assigns weights according to the categories and improves calculation formula and filling value calculation based on the MGNN (Mahalanobis-Gray and Nearest Neighbor algorithm) algorithm.The experimental results show that the filling accuracy of the method is higher than traditional KNN algorithm and MGNN algorithm.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chi Zhang
AU  - Hong-cai Fong
AU  - Kai Jin
AU  - Ting Yang
PY  - 2013/03
DA  - 2013/03
TI  - The nearest neighbor algorithm of filling missing data based on cluster analysis
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 2578
EP  - 2581
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.643
DO  - 10.2991/iccsee.2013.643
ID  - Zhang2013/03
ER  -