Ontology-Driven KDD Process Composition
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Towards an Ontology of Data Mining Investigations
DS '09 Proceedings of the 12th International Conference on Discovery Science
Workflow construction for service-oriented knowledge discovery
ISoLA'10 Proceedings of the 4th international conference on Leveraging applications of formal methods, verification, and validation - Volume Part I
A review on particle swarm optimization algorithms and their applications to data clustering
Artificial Intelligence Review
Towards an ontology of biomodelling
CMSB'12 Proceedings of the 10th international conference on Computational Methods in Systems Biology
A virtual mart for knowledge discovery in databases
Information Systems Frontiers
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Motivated by the need for unification of the field of data mining and the growing demand for formalized representation of outcomes of research, we address the task of constructing an ontology of data mining. The proposed ontology, named OntoDM, is based on a recent proposal of a general framework for data mining, and includes definitions of basic data mining entities, such as datatype and dataset, data mining task, data mining algorithm and components thereof (e.g., distance function), etc. It also allows for the definition of more complex entities, e.g., constraints in constraint-based data mining, sets of such constraints (inductive queries) and data mining scenarios (sequences of inductive queries). Unlike most existing approaches to constructing ontologies of data mining, OntoDM is a deep/heavy-weight ontology and follows best practices in ontology engineering, such as not allowing multiple inheritance of classes, using a predefined set of relations and usinga top level ontology.