Discovering temporal/causal rules: a comparison of methods
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Distinguishing causal and acausal temporal relations
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Using dependence diagrams to summarize decision rule sets
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
An intelligent decision-support model using FSOM and rule extraction for crime prevention
Expert Systems with Applications: An International Journal
The TIMERS II algorithm for the discovery of causality
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Discovering causal and temporal relations in a system is essential to understanding how it works, and to learning to control the behaviour of the system. TimeSleuth is a causality miner that uses association relations as the basis for the discovery of causal and temporal relations. It does so by introducing time into the observed data. TimeSleuth uses C4.5 as its association discoverer, and by using a series of pre-processing and post-processing techniques to enable the user to try different scenarios for mining causality. The data to be mined should originate sequentially from a single system. TimeSleuth's use of a standard decision tree builder such as C4.5 puts it outside the current mainstream method of discovering causality, which is based on conditional independencies and causal Bayesian Networks. This paper introduces TimeSleuth as a tool, and describes its functionality. TimeSleuth expands the abilities of C4.5 in some important ways. It is an unsupervised tool that can handle and interpret temporal data. It also helps the user in analyzing the relationships among the attributes by enabling him/her to see the rules, and statistics about them, in tabular form. There is also a mechanism to distinguish between causality and acausal relations. The user is thus encouraged to perform experiments and discover the nature of relationships among the data.