Statistical analysis with missing data
Statistical analysis with missing data
A unified treatment of null values using constraints
Information Sciences: an International Journal
On semantic issues connected with incomplete information databases
ACM Transactions on Database Systems (TODS)
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Maximum Consistency of Incomplete Datavia Non-Invasive Imputation
Artificial Intelligence Review
Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Classification Algorithms Based on Linear Combinations of Features
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Boolean Reasoning Scheme with Some Applications in Data Mining
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Fuzzy Similarity Relation as a Basis for Rough Approximations
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Application of Normalized Decision Measures to the New Case Classification
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
A Comparison of Several Approaches to Missing Attribute Values in Data Mining
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Incomplete Data Decomposition for Classification
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
About Tolerance and Similarity Relations in Information Systems
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
On the Extension of Rough Sets under Incomplete Information
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Rough classification in incomplete information systems
Mathematical and Computer Modelling: An International Journal
Evaluation of the decision performance of the decision rule set from an ordered decision table
Knowledge-Based Systems
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The indiscernibility relation is a fundamental concept of the rough set theory. The original definition of the indiscernibility relation does not capture the situation where some of the attribute values are missing. This paper tries to enhance former works by proposing an individual treatment of missing values at the attribute or value level. The main assumption of the theses presented in this paper considers that not all missing values are semantically equal. We propose two different approaches to create an individual indiscernibility relation for a particular information system. The first relation assumes variable, but fixed semantics of missing attribute values in different columns. The second relation assumes different semantics of missing attribute values, although this variability is limited with expressive power of formulas utilizing descriptors. We provide also a comparison of flexible indiscernibility relations and missing value imputation methods. Finally we present a simple algorithm for inducing sub-optimal relations from data.