C4.5: programs for machine learning
C4.5: programs for machine learning
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Ontology Learning for the Semantic Web
IEEE Intelligent Systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Mining Ontology for Automatically Acquiring Web User Information Needs
IEEE Transactions on Knowledge and Data Engineering
Integrating Databases into the Semantic Web through an Ontology-Based Framework
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Deriving axioms across ontologies
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
A Semi-Automatic Framework for Mining ERP Patterns
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 01
A framework to support automated classification and labeling of brain electromagnetic patterns
Computational Intelligence and Neuroscience - Regular issue
Ontology Database: A New Method for Semantic Modeling and an Application to Brainwave Data
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
An Ontology-Based Framework for Knowledge Retrieval
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Concept-Based, Personalized Web Information Gathering: A Survey
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
A knowledge-based model using ontologies for personalized web information gathering
Web Intelligence and Agent Systems
Semantic translation for rule-based knowledge in data mining
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
Using ontology databases for scalable query answering, inconsistency detection, and data integration
Journal of Intelligent Information Systems
Using information extractors with the neural electromagnetic ontologies
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Ontology of EEG mapping --- preliminary research
ITIB'12 Proceedings of the Third international conference on Information Technologies in Biomedicine
| Hi-index | 0.00 |
Event-related potentials (ERP) are brain electrophysiological patterns created by averaging electroencephalographic (EEG) data, time-locking to events of interest (e.g., stimulus or response onset). In this paper, we propose a generic framework for mining anddeveloping domain ontologies and apply it to mine brainwave (ERP) ontologies. The concepts and relationships in ERP ontologies can be mined according to the following steps: pattern decomposition, extraction of summary metrics for concept candidates, hierarchical clustering of patterns for classes and class taxonomies, and clustering-based classification and association rules mining for relationships (axioms) of concepts. We have applied this process to several dense-array (128-channel) ERP datasets. Results suggest good correspondence between mined concepts and rules, on the one hand, and patterns and rules that were independently formulated by domain experts, on the other. Data mining results also suggest ways in which expert-defined rules might be refined to improve ontologyrepresentation and classification results. The next goal of our ERP ontology mining framework is to address some long-standing challenges in conducting large-scale comparison and integration of results across ERP paradigms and laboratories. In a more general context, this work illustrates the promise of an interdisciplinary research program, which combines data mining, neuroinformatics andontology engineering to address real-world problems.