Explora: a multipattern and multistrategy discovery assistant
Advances in knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
An Algorithm for Multi-relational Discovery of Subgroups
PKDD '97 Proceedings of the First European Symposium 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
Inductive Learning for Case-Based Diagnosis with Multiple Faults
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Subgroup Discovery with CN2-SD
The Journal of Machine Learning Research
Interestingness of frequent itemsets using Bayesian networks as background knowledge
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting background knowledge for knowledge-intensive subgroup discovery
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
SD-map: a fast algorithm for exhaustive subgroup discovery
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
A methodological view on knowledge-intensive subgroup discovery
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
| Hi-index | 0.00 |
Domain knowledge is a valuable resource for improving the quality of the results of data mining methods. In this paper, we present a methodological approach for providing domain knowledge in a declarative manner : We utilize a Prolog knowledge base with facts for the specification of properties of ontological concepts and rules for the derivation of further ad-hoc relations between these concepts. This enhances the documentation , extendability , and standardization of the applied knowledge. Furthermore, the presented approach also provides for potential automatic verification and improved maintenance options with respect to the used domain knowledge.