Missing Value Filling Method Based on Fuzzy C-Means Algorithm

doi:10.13438/j.cnki.jdzk.2020.02.006

Abstract

Abstract:

For effective missing data filling, the membership matrix of fuzzy C-means algorithm is proposed as the weighted weight of the data to be filled in. Firstly, the missing data is pre-filled with the same attribute mean, then the membership matrix of each category is obtained by means of fuzzy C-means algorithm. Finally, the matrix is used as the weight to multiply the attribute mean of each category as the final filling data. In the UCI data experiment, compared with the KNN filling, the results show that the error in the method based on the fuzzy C-means algorithm filling is smaller than in the KNN filling.

Key words: missing value, fuzzy C-means algorithm, membership matrix, k-nearest neighbor

HANG Zicheng, LI Ying. Missing Value Filling Method Based on Fuzzy C-Means Algorithm[J]. Journal of Jishou University（Natural Sciences Edition), DOI: 10.13438/j.cnki.jdzk.2020.02.006.

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