Missing Value Imputation Based on Data Clustering

Zhang, S; Zhang, J; Zhu, X; Qin, Y; Zhang, C

Missing Value Imputation Based on Data Clustering

Zhang, S Zhang, J Zhu, X Qin, Y Zhang, C

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Publisher:: Springer
Publication Type:: Journal Article
Citation:: Lecture Notes in Computer Science, 2008, 4750 (2008), pp. 128 - 138
Issue Date:: 2008-01

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Field	Value	Language
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Qin, Y	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2008-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science, 2008, 4750 (2008), pp. 128 - 138	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/8987
dc.description.abstract	We propose an efficient nonparametric missing value imputation method based on clustering, called CMI (Clustering-based Missing value Imputation), for dealing with missing values in target attributes. In our approach, we impute the missing values of an instance A with plausible values that are generated from the data in the instances which do not contain missing values and are most similar to the instance A using a kernel-based method. Specifically, we first divide the dataset (including the instances with missing values) into clusters. Next, missing values of an instance A are patched up with the plausible values generated from Aâs cluster. Extensive experiments show the effectiveness of the proposed method in missing value imputation task.	en_US
dc.publisher	Springer	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0559536
dc.relation	http://purl.org/au-research/grants/arc/DP0667060
dc.relation.ispartof	Lecture Notes in Computer Science	en_US
dc.relation.isbasedon	10.1007/978-3-540-79299-4_7	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Missing Value Imputation Based on Data Clustering	en_US
dc.type	Journal Article
utslib.citation.volume	2008	en_US
utslib.citation.volume	4750	en_US
utslib.for	0804 Data Format	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.issue	2008	en_US
pubs.volume	4750	en_US

Abstract:

We propose an efficient nonparametric missing value imputation method based on clustering, called CMI (Clustering-based Missing value Imputation), for dealing with missing values in target attributes. In our approach, we impute the missing values of an instance A with plausible values that are generated from the data in the instances which do not contain missing values and are most similar to the instance A using a kernel-based method. Specifically, we first divide the dataset (including the instances with missing values) into clusters. Next, missing values of an instance A are patched up with the plausible values generated from Aâs cluster. Extensive experiments show the effectiveness of the proposed method in missing value imputation task.

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http://hdl.handle.net/10453/8987