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 language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019 |
| Publisher | IEEE Computer Society |
| Pages | 2147-2148 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781538674741 |
| DOIs | |
| State | Published - Apr 1 2019 |
| Event | 35th IEEE International Conference on Data Engineering, ICDE 2019 - Macau, China Duration: Apr 8 2019 → Apr 11 2019 |
Publication series
| Name | Proceedings - International Conference on Data Engineering |
|---|---|
| Volume | 2019-April |
| ISSN (Print) | 1084-4627 |
Conference
| Conference | 35th IEEE International Conference on Data Engineering, ICDE 2019 |
|---|---|
| Country | China |
| City | Macau |
| Period | 4/8/19 → 4/11/19 |
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All Science Journal Classification (ASJC) codes
- Software
- Signal Processing
- Information Systems
Cite this
}
Order-sensitive imputation for clustered missing values (Extended Abstract). / Ma, Qian; Gu, Yu; Lee, Wang-chien; Yu, Ge.
Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019. IEEE Computer Society, 2019. p. 2147-2148 8731477 (Proceedings - International Conference on Data Engineering; Vol. 2019-April).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Order-sensitive imputation for clustered missing values (Extended Abstract)
AU - Ma, Qian
AU - Gu, Yu
AU - Lee, Wang-chien
AU - Yu, Ge
PY - 2019/4/1
Y1 - 2019/4/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85067963755&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067963755&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2019.00268
DO - 10.1109/ICDE.2019.00268
M3 - Conference contribution
AN - SCOPUS:85067963755
T3 - Proceedings - International Conference on Data Engineering
SP - 2147
EP - 2148
BT - Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019
PB - IEEE Computer Society
ER -