C4.5: programs for machine learning
C4.5: programs for machine learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Data Analysis and Mining in Ordered Information Tables
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Proceedings of the Joint JSAI 2001 Workshop on New Frontiers in Artificial Intelligence
Research on rough set theory and applications in China
Transactions on rough sets VIII
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Work in inductive learning has mostly been concentrated on classifying. However, there are many applications in which it is desirable to order rather than to classify instances. For modelling ordering problems, we generalize the notion of information tables to ordered information tables by adding order relations in attribute values. Then we propose a data analysis model by analyzing the dependency of attributes to describe the properties of ordered information tables. The problem of mining ordering rules is formulated as finding association between orderings of attribute values and the overall ordering of objects. An 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, we first transform an ordered information table into a binary information table, and then apply any standard machine learning and data mining algorithms. As an illustration, we analyze in detail Maclean's universities ranking for the year 2000.