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Predictive Analytics and Data Mining

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Home » Methodologies » Predictive Analytics and Data Mining

Predictive Analytics and Data Mining

"Predictive Analytics" is the use of statistical techniques to analyze current and historical facts to predict future events of interest. Predictive analytics uses datasets (and sometimes what is called “big data”) to create models out of historical information by highlighting patterns and correlations and projecting those observations forward with degrees of statistical confidence.

"Data Mining" is an interdisciplinary subfield of computer science. It is the analytical computational process of discovering patterns within these datasets or "big data", and involves methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.

These methods have been applied in the past by statisticians to make predictions about business decisions, politics, public policy (most notably public health), and crime rates.

The application of predictive analytics and data mining for counterterrorism is a relatively new idea. With the explosion of social media in the past ten years, some have observed that social life is now measurable and recordable in a way that it has never been. As such, there is a growing body of literature and practice applying statistical methods to social media data to study social phenomena related to terrorism such as radicalization. 

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