MINING TEMPORAL PATTERNS IN LONGITUDINAL EVENT DATA USING DISCRETE EVENT MATRICES AND SPARSE CODING

MINING TEMPORAL PATTERNS IN LONGITUDINAL EVENT DATA USING DISCRETE EVENT MATRICES AND SPARSE CODING

Methods and systems for event pattern mining are shown that include representing longitudinal event data in a measurable geometric space as a temporal event matrix representation (TEMR) using spatial temporal shapes, wherein event data is organized into hierarchical categories of event type and perf...

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Bibliographische Detailangaben
Hauptverfasser: WANG FEI, LEE NOAH, HU JIAYING, KOHN MARTIN S, EBADOLLAHI SHAHRAM, SORRENTINO ROBERT K, SUN JIMENG
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Zusammenfassung:Methods and systems for event pattern mining are shown that include representing longitudinal event data in a measurable geometric space as a temporal event matrix representation (TEMR) using spatial temporal shapes, wherein event data is organized into hierarchical categories of event type and performing temporal event pattern mining with a processor by locating visual event patterns among the spatial temporal shapes of said TEMR using a constraint sparse coding framework.