Rough sets, rough relations and rough functions
Fundamenta Informaticae - Special issue: rough sets
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables
ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
On Efficient Handling of Continuous Attributes in Large Data Bases
Fundamenta Informaticae
Inhibitory Rules in Data Analysis: A Rough Set Approach
Inhibitory Rules in Data Analysis: A Rough Set Approach
Improving k-NN for Human Cancer Classification Using the Gene Expression Profiles
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Rule-Based Similarity for Classification
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Dominance-Based rough set approach to case-based reasoning
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
Dynamic rule-based similarity model for DNA microarray data
Transactions on Rough Sets XV
Redefinition of Decision Rules Based on the Importance of Elementary Conditions Evaluation
Fundamenta Informaticae
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This paper presents a research on discovering a similarity relation in multidimensional bioinformatic data. In particular, utilization of a Rules-based Similarity model to define a similarity in microarray datasets is discussed. The Rules-based Similarity model is a rough set extension to the feature contrast model proposed by Amos Tversky. Its main aim is to achieve high accuracy in a case-based classification task and at the same time to simulate the human way of perceiving similar objects. The similarity relation derived from the Rules-based Similarity model is suitable for genes expression profiling as the rules naturally indicate the groups of genes whose activation or inactivation is relevant in the considered context. Experiments conducted on several microarray datasets show that this model of similarity is able to capture higher-level dependencies in data and it may be successfully used in cases when the standard distance-based approach turns out to be ineffective.