Paper Info
Title
Imputation Of Missing Data Using Fuzzy Neighborhood Density-Based Clustering
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.
Year
Venue
Field
2016
2016 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE)
Data mining,Data modeling,Fuzzy clustering,Computer science,Fuzzy logic,Optimization algorithm,Artificial intelligence,Imputation (statistics),Missing data,Cluster analysis,Machine learning,Genetic algorithm
DocType
ISSN
Citations 
Conference
1544-5615
1
PageRank 
References 
Authors
0.37
17
2
Name
Order
Citations
PageRank
Roozbeh Razavi-Far19519.93
Mehrdad Saif233448.75