Large datasets: Segmentation, feature extraction, and compression
Large data sets with more than several mission multivariate observations (tens of megabytes or gigabytes of stored information) are difficult or impossible to analyze with traditional software. The amount of output which must be scanned quickly dilutes the ability of the investigator to confidently identify all the meaningful patterns and trends which may be present. The purpose of this project is to develop both a theoretical foundation and a collection of tools for automated feature extraction that can be easily customized to specific applications. Cluster analysis techniques are applied as a final step in the feature extraction process, which helps make data surveying simple and effective.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- AC05-96OR22464
- OSTI ID:
- 366463
- Report Number(s):
- ORNL/TM-13114; ON: DE96014594; TRN: 96:005154
- Resource Relation:
- Other Information: PBD: Jul 1996
- Country of Publication:
- United States
- Language:
- English
Similar Records
Analysing perturbations and nonstationarity in data series using techniques motiviated by the theory of chaotic nonlinear dynamical systems
Multi-Formalism Modeling for Disaster Resilience, Forecasting, and Response
Chemical management system at Argonne National Laboratory
Technical Report
·
Wed May 01 00:00:00 EDT 1996
·
OSTI ID:366463
+2 more
Multi-Formalism Modeling for Disaster Resilience, Forecasting, and Response
Technical Report
·
Sun Sep 01 00:00:00 EDT 2019
·
OSTI ID:366463
+7 more
Chemical management system at Argonne National Laboratory
Conference
·
Sat Jul 01 00:00:00 EDT 1995
·
OSTI ID:366463
+3 more