Temporal reasoning based on semi-intervals
Artificial Intelligence
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Maintaining knowledge about temporal intervals
Communications of the ACM
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Circle Graphs: New Visualization Tools for Text-Mining
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Adding Temporal Semantics to Association Rules
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Visualizing association rules in a framework for visual data mining
From Integrated Publication and Information Systems to Virtual Information and Knowledge Environments
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Temporal intervals and the interaction of interval-based events are fundamental in many domains including medicine, commerce, computer security and various types of normalcy analysis. In order to learn from temporal interval data we have developed a temporal interval association rule algorithm. In this paper, we will provide a definition for temporal interval association rules and present our visualisation techniques for viewing them. Visualisation techniques are particularly important because the complexity and volume of knowledge that is discovered during data mining often makes it difficult to comprehend. We adopt a circular graph for visualising a set of associations that allows underlying patterns in the associations to be identified. To visualize temporal relationships, a parallel coordinate graph for displaying the temporal relationships has been developed.