C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Relational Data Mining
Stochastic Attribute Selection Committees
AI '98 Selected papers from the 11th Australian Joint Conference on Artificial Intelligence on Advanced Topics in Artificial Intelligence
Chance Discovery
Mining diagnostic rules from clinical databases using rough sets and medical diagnostic model
Information Sciences: an International Journal - Special issue: Medical expert systems
First-Order rule mining by using graphs created from temporal medical data
AM'03 Proceedings of the Second international conference on Active Mining
Exceptions as Chance for Computational Chance Discovery
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
An interface for medical diagnosis support: from the viewpoint of chance discovery
International Journal of Advanced Intelligence Paradigms
Categorized and integrated data mining of medical data from the viewpoint of chance discovery
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
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At the International Research and Educational Institute for Integrated Medical Sciences (IREIIMS) project, we are collecting complete medical data sets to determine relationships between medical data and health status. Since the data include many items which will be categorized differently, it is not easy to generate useful rule sets. Sometimes rare rule combinations are ignored and thus we cannot determine the health status correctly. In this paper, we analyze the features of such complex data, point out the merit of categorized data mining and propose categorized rule generation and health status determination by using combined rule sets.