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pattern recognition

Contributor(s): Ivy Wigmore
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Pattern recognition is the ability to detect arrangements of characteristics or data that yield information about a given system or data set. In a technological context, a pattern might be recurring sequences of data over time that can be used to predict trends, particular configurations of features in images that identify objects, frequent combinations of words and phrases for natural language processing (NLP), or particular clusters of behavior on a network that could indicate an attack -- among almost endless other possibilities.

Pattern recognition is essential to many overlapping areas of IT, including big data analytics, biometric identification, security and artificial intelligence (AI).

Some examples of pattern recognition:

Facial recognition software takes in data related to the characteristics of a person's face and uses an algorithm to match that specific pattern to an individual record in a database.

Pattern recognition algorithms in meteorological software can detect recurring connections among weather data that can be used to forecast probable future weather events.

Network intrusion detection (NID) software rules describe patterns of behaviors and events that can indicate illegitimate traffic.

In 1997, IBM's Deep Blue used its ability to recognize patterns of play to defeat world chess champion Garry Kasparov.

In the context of AI, pattern recognition is a sub-category of machine learning (ML).

This was last updated in June 2018

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