Neural networks and open texture
ICAIL '93 Proceedings of the 4th international conference on Artificial intelligence and law
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Detecting change in legal concepts
ICAIL '95 Proceedings of the 5th international conference on Artificial intelligence and law
Knowledge discovery in the Split Up project
Proceedings of the 6th international conference on Artificial intelligence and law
The effectiveness of machine learning techniques for predicting time to case disposition
Proceedings of the 6th international conference on Artificial intelligence and law
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting change in categorical data: mining contrast sets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing association rules with interactive mosaic plots
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Identifying non-actionable association rules
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery in Databases
Knowledge Discovery in Databases
An Experiment in Discovering Association Rules in the Legal Domain
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
AOW '05 Proceedings of the 2005 Australasian Ontology Workshop - Volume 58
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Knowledge discovery from databases (KDD) exercises in law have typically attempted to derive knowledge about decision making processes in the legal domain automatically from datasets. This is made difficult in that real data that represents aspects of a decision process in law is commonly stored as text and rarely stored in structured databases. The central claim advanced here is that KDD processes can be usefully applied to existing datasets of client and demographic data in order to provide feedback for the effective operation of organizations within the legal system. However, the cost of data mining suites and the scarcity of specialized personnel for these tools mitigates against their use. In this study data mining with Association Rules (AR) has been performed on a data-set of over 380,000 records from a legal aid agency. Methods to visualise patterns in order to suggest and test plausible hypotheses from the data have been developed. The tool, called WebAssociate is entirely web based. Domain experts using the tool report favorable responses.