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

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Margaret Rouse is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical, business audience. Over the past twenty years her explanations have appeared on TechTarget websites and she's been cited as an authority in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine and Discovery Magazine.Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages. If you have a suggestion for a new definition or how to improve a technical explanation, please email Margaret or contact her…