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
N1 - Funding Information:
VI. AKNOWLEDGEMENT This work is supported by the National Key R&D Program of China (2018YFB1003404) and the National Nature Science Foundation of China (61433008, U1435216, 61872070 and 61472071).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/4
Y1 - 2019/4
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
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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
T2 - 35th IEEE International Conference on Data Engineering, ICDE 2019
Y2 - 8 April 2019 through 11 April 2019
ER -