Ontology-guided knowledge discovery in databases
Proceedings of the 1st international conference on Knowledge capture
Interestingness of frequent itemsets using Bayesian networks as background knowledge
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering interesting patterns through user's interactive feedback
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining temporal patterns from sequence database of interval-based events
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Discovering richer temporal association rules from interval-based data
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Finding Explanations for Assisting Pattern Interpretation
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Using ontologies to facilitate post-processing of association rules by domain experts
Information Sciences: an International Journal
Domain-driven KDD for mining functionally novel rules and linking disjoint medical hypotheses
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
SEWEBAR-CMS: semantic analytical report authoring for data mining results
Journal of Intelligent Information Systems
Computer Methods and Programs in Biomedicine
Context-based knowledge discovery and its application
DM-IKM '12 Proceedings of the Data Mining and Intelligent Knowledge Management Workshop
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This paper reports a domain ontology-driven approach to data mining on a medical database containing clinical data on patients undergoing treatment for chronic kidney disease. Each record within the dataset is comprised of a large number (up to 96) of quantitative and qualitative metrics which represent the physiological state of a particular patient on a particular day of treatment. One of the challenges of mining such a dataset is that the meaning of many of the metrics/attributes is not easily understood by someone who is not familiar with the domain of kidney disease and treatment, and it is not clear which of the attributes are useful in data mining. This paper explores the possibility of utilizing a medical domain ontology as a source of domain knowledge to aid in both extracting knowledge and expressing the extracted knowledge in a useful format. We describe an approach in which the domain ontology is used to categorize attributes in preparation for mining 'association rules' in the data; the mined rules were then reviewed by comparison to domain knowledge derived from a domain expert in order to gauge their 'usefulness'. We conclude that domain ontology driven data mining can obtain more meaningful results than naïve mining.