@inproceedings{5541c53b9f1d4b44aa98aa32992c306e,
title = "Visualisation of Temporal Interval Association Rules",
abstract = "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.",
author = "Rainsford, {Chris P.} and Roddick, {John F.}",
year = "2000",
month = dec,
doi = "10.1007/3-540-44491-2_14",
language = "English",
isbn = "3540414509",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "91--96",
editor = "Helen Meng and Leung, {Kwong Sak} and Lai-Wan Chan",
booktitle = "IDEAL '00",
note = "2nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2000 ; Conference date: 13-12-2000 Through 15-12-2000",
}