Order-sensitive imputation for clustered missing values (Extended Abstract)

Qian Ma, Yu Gu, Wang Chien Lee, Ge Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

To study the issue of missing values (MVs), we propose the Order-Sensitive Imputation for Clustered Missing values (OSICM) framework, in which missing values are imputed sequentially such that the values filled earlier in the process are also used for later imputation of other MVs. Obviously, the order of imputations is critical to the effectiveness and efficiency of OSICM framework. We formulate the searching of the optimal imputation order as an optimization problem, and show its NP-hardness. Furthermore, we devise an algorithm to find the exact optimal solution and propose two approximate/heuristic algorithms to trade off effectiveness for efficiency. Finally, we conduct extensive experiments on real and synthetic datasets to demonstrate the superiority of our OSICM framework.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019
PublisherIEEE Computer Society
Pages2147-2148
Number of pages2
ISBN (Electronic)9781538674741
DOIs
StatePublished - Apr 2019
Event35th IEEE International Conference on Data Engineering, ICDE 2019 - Macau, China
Duration: Apr 8 2019Apr 11 2019

Publication series

NameProceedings - International Conference on Data Engineering
Volume2019-April
ISSN (Print)1084-4627

Conference

Conference35th IEEE International Conference on Data Engineering, ICDE 2019
CountryChina
CityMacau
Period4/8/194/11/19

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems

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  • Cite this

    Ma, Q., Gu, Y., Lee, W. C., & Yu, G. (2019). Order-sensitive imputation for clustered missing values (Extended Abstract). In Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019 (pp. 2147-2148). [8731477] (Proceedings - International Conference on Data Engineering; Vol. 2019-April). IEEE Computer Society. https://doi.org/10.1109/ICDE.2019.00268