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Characterization
Last Updated: January 18, 2017
Definition - What does Characterization mean?
Characterization is a big data methodology that is used for generating descriptive parameters that effectively describe the characteristics and behavior of a particular data item. This is then used in unsupervised learning algorithms in order to find patterns, clusters and trends without incorporating class labels that may have biases. It has its uses in cluster analysis and even deep learning.
Techopedia explains Characterization
Big data characterization is a technique for transforming raw data into useful information, being used in machine learning algorithms and data mining. Characterization essentially generates condensed representations of whatever information content is hidden within data. Therefore, it can be used as a means of measuring and tracking events, changes and new emergent behaviors in large dynamic data streams.
Some benefits of characterization:
- Can generate useful metrics for tracking and measuring events and anomalies in data sets
- Creates small footprint representations of essential information
- Quickly accomplishes data-to-information conversion, which brings the industry closer to the full data-to-information-to-knowledge transformation
- Is useful for indexing and tagging specific objects, events and other features in a data collection
