C4.5: programs for machine learning
C4.5: programs for machine learning
Numeric and symbolic data fusion: a soft computing approach to remote sensing images analysis
Pattern Recognition Letters - Special issue on non-conventional pattern analysis in remote sensing
Data preparation for data mining
Data preparation for data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Cluster-Based Algorithms for Dealing with Missing Values
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
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
Discovering Knowledge in Data: An Introduction to Data Mining
Discovering Knowledge in Data: An Introduction to Data Mining
Possibilistic evidential clustering
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Digestive casebase mining based on possibility theory and linear unidimensional scaling
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Possibilistic pattern recognition in a digestive database for mining imperfect data
WSEAS TRANSACTIONS on SYSTEMS
Inferring a possibility distribution from empirical data
Fuzzy Sets and Systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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To estimate the missing values of an attribute in the records of a dataset, all the information provided by the other attributes and the knowledge databases must be considered. However, the information elements could be imperfect (imprecise, possibilistic, probabilistic, etc.) and could have different measuring scales (quantitative, qualitative, ordinal, etc.) at the same time. Furthermore, the relationships and the correlation between the considered attribute and the others should also be pondered. Unlike the prior works that have separately processed these issues using complex and conditional techniques, our approach, essentially based on the tools provided by the possibility theory, can easily handle these aspects within a unified, robust, and simple frameworks. Several numeric examples and applications have been given to simply illustrate the main steps of our method, and some promising perspectives have been proposed at the end of this paper.