C4.5: programs for machine learning
C4.5: programs for machine learning
A database perspective on knowledge discovery
Communications of the ACM
Communications of the ACM
Modeling KDD Processes within the Inductive Database Framework
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Tutorial on ontological engineering: part 3: Advanced course of ontological engineering
New Generation Computing - Grid systems for life sciences
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The EXACT description of biomedical protocols
Bioinformatics
Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
OntoDM: An Ontology of Data Mining
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Semantic Annotation and Services for KDD Tools Sharing and Reuse
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Towards a general framework for data mining
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Ontology Learning and Population: Bridging the Gap between Text and Knowledge - Volume 167 Frontiers in Artificial Intelligence and Applications
Experiment databases: a novel methodology for experimental research
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Towards an ontology of biomodelling
CMSB'12 Proceedings of the 10th international conference on Computational Methods in Systems Biology
A survey of intelligent assistants for data analysis
ACM Computing Surveys (CSUR)
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Motivated by the need for unification of the domain of data mining and the demand for formalized representation of outcomes of data mining investigations, we address the task of constructing an ontology of data mining. In this paper we present an updated version of the OntoDM ontology, that is based on a recent proposal of a general framework for data mining and it is aligned with the ontology of biomedical investigations (OBI) . The ontology aims at describing and formalizing entities from the domain of data mining and knowledge discovery. It includes definitions of basic data mining entities (e.g., datatype, dataset, data mining task, data mining algorithm etc.) and allows extensions with more complex data mining entities (e.g. constraints, data mining scenarios and data mining experiments). Unlike most existing approaches to constructing ontologies of data mining, OntoDM is compliant to best practices in engineering ontologies that describe scientific investigations (e.g., OBI ) and is a step towards an ontology of data mining investigations. OntoDM is available at: http://kt.ijs.si/panovp/OntoDM/ .