Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Interestingness via what is not interesting
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Database Systems (TODS)
Discovering the set of fundamental rule changes
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
Heuristic Measures of Interestingness
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Handling very large numbers of association rules in the analysis of microarray data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Guiding knowledge discovery through interactive data mining
Managing data mining technologies in organizations
Generating an informative cover for association rules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Exploratory medical knowledge discovery: experiences and issues
ACM SIGKDD Explorations Newsletter
Detecting privacy and ethical sensitivity in data mining results
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
Post-analysis of learned rules
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Visual Analytics: A 2D-3D visualization support for human-centered rule mining
Computers and Graphics
Interactive visual exploration of association rules with rule-focusing methodology
Knowledge and Information Systems
| Hi-index | 0.00 |
Practical knowledge discovery is an iterative process. First, the experiences gained from one mining run are used to inform the parameter setting and the dataset and attribute selection for subsequent runs. Second, additional data, either incremental additions to existing datasets or the inclusion of additional attributes means that the mining process is reinvoked, perhaps numerous times. Reducing the number of iterations, improving the accuracy of parameter setting and making the results of the mining run more clearly understandable can thus significantly speed up the discovery process.In this paper we discuss our experiences in this area and present a system that helps the user to navigate through association rule result sets in a way that makes it easier to find useful results from a large result set. We present several techniques that experience has shown us to be useful. The prototype system -- IRSetNav -- is discussed, which has capabilities in redundant rule reduction, subjective interestingness evaluation, item and itemset pruning, related information searching, text-based itemset and rule visualisation, hierarchy based searching and tracking changes between data sets using a knowledge base. Techniques also discussed in the paper, but not yet accommodated into IRSetNav, include input schema selection, longitudinal ruleset analysis and graphical visualisation techniques.