The Nearest Neighbor Algorithm of Filling Missing Data Based on Cluster Analysis

Chi Zhang; Jin Kai; Hong Cai Feng; Tin Yang

doi:10.4028/www.scientific.net/AMM.347-350.2324

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 347-350The Nearest Neighbor Algorithm of Filling Missing...

The Nearest Neighbor Algorithm of Filling Missing Data Based on Cluster Analysis

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.

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Periodical:

Applied Mechanics and Materials (Volumes 347-350)

Pages:

2324-2328

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.347-350.2324

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Online since:

August 2013

Authors:

Chi Zhang, Jin Kai, Hong Cai Feng, Tin Yang

Keywords:

Cluster Analysis, Grey Correlation, Mahalanobis Distance, Maximum, Nearest Neighbor Alorithm

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