Measuring retrieval effectiveness based on user preference of documents
Journal of the American Society for Information Science
Automated Discovery of Plausible Rules Based on Rough Sets and Rough Inclusion
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
An Analysis of Quantitative Measures Associated with Rules
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Journal of Artificial Intelligence Research
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Analyzing and mining ordered information tables
Journal of Computer Science and Technology
Interpreting Low and High Order Rules: A Granular Computing Approach
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Conceptual modeling rules extracting for data streams
Knowledge-Based Systems
Research on rough set theory and applications in China
Transactions on rough sets VIII
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Many real world problems deal with ordering of objects instead of classifying objects, although majority of research in machine learning and data mining has been focused on the latter. In this paper, we formulate the problem of mining ordering rules as finding association between orderings of attribute values and the overall ordering of objects. An example of ordering rules may state that "if the value of an object x on an attribute a is ordered ahead of the value of another object y on the same attribute, then x is ordered ahead of y". For mining ordering rules, the notion of information tables is generalized to ordered information tables by adding order relations on attribute values. Such a table can be transformed into a binary information table, on which any standard data mining algorithm can be used.