Towards an open architecture for LDL
VLDB '89 Proceedings of the 15th international conference on Very large data bases
Inductive databases and condensed representations for data mining (extended abstract)
ILPS '97 Proceedings of the 1997 international symposium on Logic programming
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Optimization of queries with user-defined predicates
ACM Transactions on Database Systems (TODS)
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications
Data Mining and Knowledge Discovery
Nondeterministic, Nonmonotonic Logic Databases
IEEE Transactions on Knowledge and Data Engineering
Querying Inductive Databases via Logic-Based User-Defined Aggregates
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Fast algorithms for mining association rules and sequential patterns
Fast algorithms for mining association rules and sequential patterns
LDL-Mine: Integrating Data Mining with Intelligent Query Answering
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
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We present a way of exploiting domain knowledge in the design and implementation of data mining algorithms, with special attention to frequent patterns discovery, within a deductive framework. In our framework domain knowledge is represented by deductive rules, and data mining algorithms are constructed by means of iterative user-defined aggregates. Iterative user-defined aggregates have a fixed scheme that allows the modularization of data mining algorithms, thus providing a way to exploit domain knowledge in the right point. As a case study, the paper presents user-defined aggregates for specifying a version of the apriori algorithm. Some performance analyses and comparisons are discussed in order to show the effectiveness of the approach.