Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining confident rules without support requirement
Proceedings of the tenth international conference on Information and knowledge management
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Finding Interesting Associations without Support Pruning
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining association rules on significant rare data using relative support
Journal of Systems and Software
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Confident Minimal Rules with Fixed-Consequents
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Mining interesting imperfectly sporadic rules
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Finding sporadic rules using apriori-inverse
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Most previous research into association rule data mining has focused on finding frequent rules; rules with high support and high confidence. However detecting rare or sporadic association rules, which have low support and high confidence, is a worthwhile task as well, as they represent rare, but potentially interesting and important associations. Mining rare or sporadic rules is a difficult data mining problem, and most previous approaches use an Apriori [1] like method [2-9]. However in order for Apriori to find rare rules, minimum support must be set very low, which results in a large amount of redundant rules and a long runtime. We previously proposed the Apriori-Inverse [10] and MIISR [11] algorithms to find sporadic rules quickly and efficiently. This paper provides an insight into the qualitative results produced by our proposed algorithms. We explore a specific real-world case study in more detail to get a qualitative understanding, namely the Dermatology dataset, for the diagnosis of the Erythemato-Squamous diseases. We show that traditional unmodified Apriori is not well suited to this task, and that our proposed algorithms are capable of producing interesting sporadic rules without having any expert domain knowledge.