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Investigative Data Mining: Mathematical Models for Analyzing, Visualizing and Destabilizing Terrorist Networks

von Nasrullah Memon

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[1.] Nm/Fragment 024 01 - Diskussion
Zuletzt bearbeitet: 2012-04-29 22:21:50 Hindemith
BauernOpfer, DeRosa 2004, Fragment, Gesichtet, Nm, SMWFragment, Schutzlevel sysop

Typus
BauernOpfer
Bearbeiter
Graf Isolan
Gesichtet
Yes
Untersuchte Arbeit:
Seite: 24, Zeilen: 4-20
Quelle: DeRosa 2004
Seite(n): v, Zeilen: 19-25, 32-38
Data mining actually has relatively narrow meaning: the approach that uses algorithms to determine analytical patterns in datasets. Subject-based automated data analysis applies models to data to predict behaviour, assess risk, determine associations, or do other type of analysis (DeRosa Mary, 2004). The models used for automated data analysis can be used on patterns discovered by data mining techniques.

Although these techniques are powerful, it is a mistake to view investigative data mining techniques as complete solution to security problems. The strength of investigative data mining (IDM) is to assist analysts and investigators. IDM can automate some tasks that analysts would otherwise have to accomplish manually. It can help to place in order attention and focus an inquiry, and can even do some early analysis and sorting of masses of data. Nevertheless, in the multifaceted world of counterterrorism, it is not likely to be useful as the only source for a conclusion or decision.

“Data mining” actually has a relatively narrow meaning: it is a process that uses algorithms to discover predictive patterns in data sets. “Automated data analysis” applies models to data to predict behavior, assess risk, determine associations, or do other types of analysis. The models used for automated data analysis can be based on patterns (from data mining or discovered by other methods) or subject based, which start with a specific known subject. [...]

Although these techniques are powerful, it is a mistake to view data mining and automated data analysis as complete solutions to security problems. Their strength is as tools to assist analysts and investigators. They can automate some functions that analysts would otherwise have to perform manually, they can help prioritize attention and focus an inquiry, and they can even do some early analysis and sorting of masses of data. But in the complex world of counterterrorism, they are not likely to be useful as the only source for a conclusion or decision.

Anmerkungen

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Letzte Bearbeitung dieser Seite: durch Benutzer:Hindemith, Zeitstempel: 20120429223116