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Typus
Verschleierung
Bearbeiter
Graf Isolan
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Untersuchte Arbeit:
Seite: 26, Zeilen: 14-23
Quelle: Popp and Poindexter 2006
Seite(n): 23, Zeilen: left column 5-16
Traditional data mining normally refers to using techniques rooted in statistics, rule-based logic, or artificial intelligence, machine learning, fuzzy logic, statistics to examine through large amounts of data to find previously unknown but statistically significant patterns. However, the application of IDM in the counterterrorism domain is more challenging, because unlike traditional data mining applications, we must find extremely wide variety of activities and hidden relationships among individuals (Seifert 2006). Table 1.1 gives a series of reasons why traditional data mining is not the same as investigative data mining. Data mining commonly refers to using techniques rooted in statistics, rule-based logic, or artificial intelligence to comb through large amounts of data to discover previously unknown but statistically significant patterns. However, the general counterterrorism problem is much harder because unlike commercial data mining applications, we must find extremely rare instances of patterns across an extremely wide variety of activities and hidden relationships among individuals. Table 2 gives a series of reasons for why commercial data mining isn’t the same as terrorism detection in this context.
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