DocumentCode :
3697223
Title :
A Hybrid Method for Incomplete Data Imputation
Author :
Liang Zhao;Zhikui Chen;Zhennan Yang;Yueming Hu
Author_Institution :
Sch. of Software Technol., Dalian Univ. of Technol., Dalian, China
fYear :
2015
Firstpage :
1725
Lastpage :
1730
Abstract :
With the explosive increase of data volume, the research of data quality and data usability draws extensive attention. In this work, we focus on one aspect of data usability -- incomplete data imputation, and present a novel missing value imputation method using stacked auto-encoder and incremental clustering (SAICI). Specifically, SAICI´s functionality rests on four pillars: (i) a distinctive value assigned to impute missing values initially, (ii) the stacked auto-encoder(SAE) applied to locate principal features, (iii) a new incremental clustering utilized to partition incomplete data set, and (iv) the top nearest neighbors´ weighted values designed to refill the missing values. Most importantly, stages (ii)~(iv) iterate until convergence condition is satisfied. Experimental results demonstrate that the proposed scheme not only imputes the missing data values effectively, but also has better time performance. Moreover, this work is suitable for distributed data processing framework, which can be applied to the imputation of incomplete big data.
Keywords :
"Clustering algorithms","Partitioning algorithms","Algorithm design and analysis","Accuracy","Feature extraction","Integrated circuits","Time complexity"
Publisher :
ieee
Conference_Titel :
High Performance Computing and Communications (HPCC), 2015 IEEE 7th International Symposium on Cyberspace Safety and Security (CSS), 2015 IEEE 12th International Conferen on Embedded Software and Systems (ICESS), 2015 IEEE 17th International Conference on
Type :
conf
DOI :
10.1109/HPCC-CSS-ICESS.2015.103
Filename :
7336420
Link To Document :
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