Data mining
Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Relational Data Mining
Relational rule induction with CPROGO14.4: a tutorial introductuon
Relational Data Mining
Learning Logical Definitions from Relations
Machine Learning
Rule Discovery in Large Time-Series Medical Databases
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Learning First Order Logic Time Series Classifiers: Rules and Boosting
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Feature Selection for Temporal Health Records
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Active subgroup mining: a case study in coronary heart disease risk group detection
Artificial Intelligence in Medicine
Visualization and interactive analysis of blood parameters with InfoZoom
Artificial Intelligence in Medicine
Machine learning for medical diagnosis: history, state of the art and perspective
Artificial Intelligence in Medicine
Data mining of multi-categorized data
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
| Hi-index | 0.00 |
In managing medical data, handling time-series data, which contain irregularities, presents the greatest difficulty. In the present paper, we propose a first-order rule discovery method for handling such data. The present method is an attempt to use graph structure to represent time-series data and reduce the graph using specified rules for inducing hypothesis. In order to evaluate the proposed method, we conducted experiments using real-world medical data.