HISSCLU: a hierarchical density-based method for semi-supervised clustering
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Automatic Detection of Erythemato-Squamous Diseases Using k-Means Clustering
Journal of Medical Systems
Detecting activities from body-worn accelerometers via instance-based algorithms
Pervasive and Mobile Computing
Proceedings of the 21st ACM international conference on Information and knowledge management
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Motivation: Classification is an important data mining task in biomedicine. In particular, classification on biomedical data often claims the separation of pathological and healthy samples with highest discriminatory performance for diagnostic issues. Even more important than the overall accuracy is the balance of a classifier, particularly if datasets of unbalanced class size are examined. Results: We present a novel instance-based classification technique which takes both information of different local density of data objects and local cluster structures into account. Our method, which adopts the basic ideas of density-based outlier detection, determines the local point density in the neighborhood of an object to be classified and of all clusters in the corresponding region. A data object is assigned to that class where it fits best into the local cluster structure. The experimental evaluation on biomedical data demonstrates that our approach outperforms most popular classification methods. Availability: The algorithm LCF is available for testing under http://biomed.umit.at/upload/lcfx.zip Contact: christian.baumgartner@umit.at