Probabilistic induction by dynamic part generation in virtual trees
Proceedings of Expert Systems '86, The 6Th Annual Technical Conference on Research and development in expert systems III
Data preparation for data mining
Data preparation for data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Machine Learning
Techniques for Dealing with Missing Values in Classification
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
On decomposition for incomplete data
Fundamenta Informaticae
AN EMPIRICAL COMPARISON OF TECHNIQUES FOR HANDLING INCOMPLETE DATA USING DECISION TREES
Applied Artificial Intelligence
Dynamic Clustering-Based Estimation of Missing Values in Mixed Type Data
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
An Investigation of Missing Data Methods for Classification Trees Applied to Binary Response Data
The Journal of Machine Learning Research
Possibilistic missing data estimation
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Towards a possibilistic processing of missing values under complex conditions
WSEAS Transactions on Information Science and Applications
Dealing with missing data: algorithms based on fuzzy set and rough set theories
Transactions on Rough Sets IV
Data Envelopment Analysis of clinics with sparse data: Fuzzy clustering approach
Computers and Industrial Engineering
On Decomposition for Incomplete Data
Fundamenta Informaticae
The quick dynamic clustering method for mixed-type data
Automation and Remote Control
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We first survey existing methods to deal with missing values and report the results of an experimental comparative evaluation in terms of their processing cost and quality of imputing missing values. We then propose three cluster-based mean-and-mode algorithms to impute missing values. Experimental results show that these algorithms with linear complexity can achieve comparative quality as sophisticated algorithms and therefore are applicable to large datasets.