Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Inductive databases and condensed representations for data mining (extended abstract)
ILPS '97 Proceedings of the 1997 international symposium on Logic programming
Towards on-line analytical mining in large databases
ACM SIGMOD Record
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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
Query driven knowledge discovery in multidimensional data
Proceedings of the 2nd ACM international workshop on Data warehousing and OLAP
Machine Learning
Nondeterministic, Nonmonotonic Logic Databases
IEEE Transactions on Knowledge and Data Engineering
Querying Inductive Databases: A Case Study on the MINE RULE Operator
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
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
Efficient C4.5
LDL-Mine: Integrating Data Mining with Intelligent Query Answering
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
A relational query primitive for constraint-based pattern mining
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
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The paper shows how a logic-based database language can support the various steps of the KDD process by providing a high degree of expressiveness, and the separation of concerns between the specification level and the mapping to the underlying databases and data mining tools. In particular, the mechanism of user-defined aggregates provided in LDL++ allows to specify data mining tasks and to formalize the mining results in a uniform way. We show how the mechanism applies to the concept of Inductive Databases, proposed in [2, 12]. We concentrate on bayesian classification and show how user defined aggregates allow to specify the mining evaluation functions and the returned patterns. The resulting formalism provides a flexible way to customize, tune and reason on both the evaluation functions and the extracted knowledge.