Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Selecting and reporting what is interesting
Advances in knowledge discovery and data mining
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Mining the Knowledge Mine: The Hot Spots Methodology for Mining Large Real World Databases
AI '97 Proceedings of the 10th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Journal of Artificial Intelligence Research
The Integrated Delivery of Large-Scale Data Mining: The ACSys Data Mining Project
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
Discovery of interesting regions in spatial data sets using supervised clustering
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
A prescription fraud detection model
Computer Methods and Programs in Biomedicine
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Data Mining delivers novel and useful knowledge from very large collections of data. The task is often characterised as identifying key areas within a very large dataset which have some importance or are otherwise interesting to the data owners. We call this hot spots data mining. Data mining projects usually begin with ill-defined goals expressed vaguely in terms of making interesting discoveries. The actual goals are refined and clarified as the process proceeds. Data mining is an exploratory process where the goals may change and such changes may impact the data space being explored. In this paper we introduce an approach to data mining where the development of the goal itself is part of the problem solving process. We propose an evolutionary approach to hot spots data mining where both the measure of interestingness and the descriptions of groups in the data are evolved under the influence of a user guiding the system towards significant discoveries.