The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Information Sciences: an International Journal
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rough Sets in Knowledge Discovery 2: Applications, Case Studies, and Software Systems
Rule Discovery in Large Time-Series Medical Databases
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
A Frequent Patterns Tree Approach for Rule Generation with Categorical Septic Shock Patient Data
ISMDA '01 Proceedings of the Second International Symposium on Medical Data Analysis
Evaluating the correlation between objective rule interestingness measures and real human interest
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Artificial Intelligence in Medicine
Optimonotone Measures For Optimal Rule Discovery
Computational Intelligence
Improving the interpretability of classification rules discovered by an ant colony algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Comprehensible classification models: a position paper
ACM SIGKDD Explorations Newsletter
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Since early 1980's, the rapid growth of hospital information systems stores the large amount of laboratory examinations as databases. Thus, it is highly expected that knowledge discovery and data mining(KDD) methods will find interesting patterns from databases as reuse of stored data and be important for medical research and practice because human beings cannot deal with such a huge amount of data. However, there are still few empirical approaches which discuss the whole data mining process from the viewpoint of medical data. In this paper, KDD process from a hospital information system is presented by using two medical datasets. This empirical study show that preprocessing and data projection are the most time-consuming processes, in which very few data mining researches have not dicussed yet and that application of rule induction methods is much easier than preprocessing.