Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Exploratory mining via constrained frequent set queries
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-level organization and summarization of the discovered rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
DMajor—Application Programming Interface for Database Mining
Data Mining and Knowledge Discovery
ICSC '99 Proceedings of the 5th International Computer Science Conference on Internet Applications
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
ACM SIGKDD Explorations Newsletter
Data Mining of Association Rules and the Process of Knowledge Discovery in Databases
Industrial Conference on Data Mining: Advances in Data Mining, Applications in E-Commerce, Medicine, and Knowledge Management
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Knowledge discovery in databases is a complex, iterative, and highly interactive process. When mining for association rules, typically interactivity is largely smothered by the execution times of the rule generation algorithms. Our approach is to accept a single, possibly expensive run, but all subsequent mining queries are supposed to be answered interactively by accessing a sophisticated rule cache. However there are two critical aspects. First, access to the cache must be efficient and comfortable. Therefore we enrich the basic association mining framework by descriptions of items through application dependent attributes. Furthermore we extend current mining query languages to deal with these attributes through 驴 and 驴 quantifiers. Second, the cache must be prepared to answer a broad variety of queries without rerunning the mining algorithm. A main contribution of this paper is that we show how to postpone restrict operations on the transactions from rule generation to rule retrieval from the cache. That is, without actually rerunning the algorithm, we efficiently construct those rules from the cache that would have been generated if the mining algorithm were run on only a subset of the transactions. In addition we describe how we implemented our ideas on a conventional relational database system. We evaluate our prototype concerning response times in a pilot application at DaimlerChrysler. It turns out to satisfy easily the demands of interactive data mining.