Data mining and criminal intelligence: a new era in crime prevention

Even if Philip K. Dick’s “Precrime is not yet a reality, today’s data mining and criminal intelligence prevention techniques are radically changing crime fighting operations, helping both local and national law enforcement agencies to better prevent and prosecute crime.

The terrorist acts of September 11, 2001 transformed our world. Since then, homeland security concerns have increased dramatically in all western countries. As a result, law enforcement agencies and local authorities have begun to collect massive amounts of information and data in an effort to prevent future attacks.

The U.S. is probably the most active nation in this field, even prior to 9/11. As one of the first experimental crime databases, the Coplink project was created by the University of Arizona’s Artificial Intelligence Lab and the Tucson Police Department, and funded by the National Department of Justice in 1997.

Coplink showed how data mining and criminal intelligence techniques can be used to dramatically improve the effectiveness of crime prevention activities and the prosecution of criminals by sharing information between different law enforcement agencies and using analytical tools to gather information.

For example, it demonstrated how data mining can be a powerful tool for helping investigators and crime analysts to examine large databases in seconds, providing information and insight necessary for preventing crime, quickly and with fewer errors.

Data mining and criminal intelligence techniques

The Coplink project experimented with a variety of the most commonly used data mining and criminal intelligence techniques, including the following:

Entity extraction: Commonly used to automatically identify people, organizations, vehicles and personal details in unstructured data such as police reports. Even if entity extraction provides only basic information, it can accelerate the investigation by rapidly providing precise details from large amounts of unstructured data.

Clustering techniques: Clustering techniques are used to group similar characteristics together  in classes in order to gain intelligence by maximizing or minimizing similarities; for example, to identify suspects or criminal groups conducting crimes in similar ways. Clustering techniques could be effectively applied through conceptual space algorithms to discover criminal relations by cross referencing entities in criminal records.

Association rules: This data mining technique has been used to discover recurring items in databases in order to create pattern rules and detect potential future events. This technique has been effective in preventing network intrusions and attacks, such as denial of service attacks (DDoS).

Sequential pattern mining: as association rule it is useful to identify sequences or recurring item in order to define patterns and prevent attacks, in network security.

Classification: This technique is useful for analyzing unstructured data to discover common properties among criminal entities. Classification has been used together with inferential statistics techniques to predict crime trends. This technique can dramatically narrow down different criminal entities and organize them into predefined classes.

String comparison: This technique is used to reveal deceptive information in criminal records by comparing structured text fields. This requires highly intensive computational capabilities.

Text mining and criminal intelligence

As many police records systems contain large volumes of unstructured data, text mining techniques are the next step in the evolution of data mining and criminal intelligence technologies. Expert System’s Cogito Intelligence Platform is a great example of a linguistic-analysis tool that can be applied to a criminal knowledge domain to perform smarter, actionable analysis on large unstructured datasets to prevent crime.

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