Similarity measures in scientometric research: the Jaccard index versus Salton's cosine formula
Information Processing and Management: an International Journal
Data & Knowledge Engineering
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
iMAP: discovering complex semantic matches between database schemas
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Development of NeuroElectroMagnetic ontologies(NEMO): a framework for mining brainwave ontologies
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Data Mining and Knowledge Discovery
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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In the present paper, we use data mining methods to address two challenges in the sharing and integration of data from electrophysiological (ERP) studies of human brain function. The first challenge, ERP metric matching, is to identify correspondences among distinct summary features (''metrics'') in ERP datasets from different research labs. The second challenge, ERP pattern matching, is to align the ERP patterns or ''components'' in these datasets. We address both challenges within a unified framework. The utility of this framework is illustrated in a series of experiments using ERP datasets that are designed to simulate heterogeneities from three sources: (a) different groups of subjects with distinct simulated patterns of brain activity, (b) different measurement methods, i.e, alternative spatial and temporal metrics, and (c) different patterns, reflecting the use of alternative pattern analysis techniques. Unlike real ERP data, the simulated data are derived from known source patterns, providing a gold standard for evaluation of the proposed matching methods. Using this approach, we demonstrate that the proposed method outperforms well-known existing methods, because it utilizes cluster-based structure and thus achieves finer-grained representation of the multidimensional (spatial and temporal) attributes of ERP data.