Title

Imputation of missing data using fuzzy neighborhood density-based clustering

Document Type

Conference Proceeding

Publication Date

11-7-2016

Publication Title

2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016

First Page

1834

Last Page

1841

Abstract

Imputation of missing data is of paramount importance in machine learning and data mining tasks with incomplete data. In this paper, a fuzzy-neighborhood density-based clustering technique is developed for imputation of missing data. The proposed technique makes use of the density measure, in order to group the similar patterns and find the best donors for each incomplete target pattern to impute its missing values. The fuzzy neighborhood membership degrees are adjusted using an invasive weed optimization algorithm. The performance of the proposed imputation technique is evaluated using eight synthetic publicly available datasets with induced missing values and compared with the performance of other existing competitors, k-means imputation, fuzzy c-means imputation and fuzzy c-means with genetic algorithm imputation. Various types of missingness have been induced to each dataset. The attained results show the effectiveness of the proposed missing data imputation technique.

DOI

10.1109/FUZZ-IEEE.2016.7737913

ISBN

9781509006250

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