An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. Keogh, E. J & Pazzani, M. J
abstract   bibtex   
We introducean extendedrepresentationof time seriesthat allows fast, accurateclassificationand clusteringin addition to the ability to explore time series data in a relevance feedbackframework. The representationconsistsof piecewise linear segmentsto representshapeand a weight vector that contains the relative importance of each individual linear segment.In the classificationcontext,the weightsare learnedautomaticallyas part of the training cycle. In the relevancefeedbackcontext, the weights are determinedby an interactive and iterative process in which users rate various choices presented to them. Our representation allows a user to define a variety of similarity measuresthat can be tailored to specific domains. We demonstrateour approachon spacetelemetry,medicalandsyntheticdata.
@article{keogh_enhanced_nodate,
	title = {An {Enhanced} {Representation} of {Time} {Series} {Which} {Allows} {Fast} and {Accurate} {Classification}, {Clustering} and {Relevance} {Feedback}},
	abstract = {We introducean extendedrepresentationof time seriesthat allows fast, accurateclassificationand clusteringin addition to the ability to explore time series data in a relevance feedbackframework. The representationconsistsof piecewise linear segmentsto representshapeand a weight vector that contains the relative importance of each individual linear segment.In the classificationcontext,the weightsare learnedautomaticallyas part of the training cycle. In the relevancefeedbackcontext, the weights are determinedby an interactive and iterative process in which users rate various choices presented to them. Our representation allows a user to define a variety of similarity measuresthat can be tailored to specific domains. We demonstrateour approachon spacetelemetry,medicalandsyntheticdata.},
	language = {en},
	author = {Keogh, Eamonn J and Pazzani, Michael J},
	pages = {9}
}
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