crime data mining

INTELLIGENT DATA MINING TECHNIQUES

Traditional data mining techniques such as association analysis, classification and prediction, cluster analysis, and outlier analysis identify patterns in structured data.3 Newer techniques identify patterns from both structured and unstructured data. As with other forms of data mining, crime data mining raises privacy concerns.

For Nevertheless,researchers have developed various automated data mining techniques for both local law enforcement and national security applications. Entity extraction identifies particular patterns from data such as text, images, or audio materials. It has been used to automatically identify persons, addresses, vehicles, and personal characteristics from police narrative reports.5 In computer forensics, the extraction of software metrics which includes the data structure, program flow, organization and quantity of comments, and use of variable names can facilitate further investigation by, for example, grouping similar programs written by hackers and tracing their behavior. Entity extraction provides basic information for crime analysis, but its performance depends greatly on the availability of extensive amounts of clean input data. 

Clustering techniques group data items into classes with similar characteristics to maximize or minimize intraclass similarity for example, to identify suspects who conduct crimes in similar ways or distinguish among groups belonging to different gangs. These techniques do not have a set of predefined classes for assigning items. Some researchers use the statistics-based concept space algorithm to automatically associate different objects such as persons, organizations, and vehicles in crime records. Using link analysis techniques to identify similar transactions, the Financial Crimes Enforcement Network AI System8 exploits Bank Secrecy Act data to support the detection and analysis of money laundering and other financial crimes. Clustering crime incidents can automate a major part of crime analysis but is limited by the high computational intensity typically required. 

Association rule mining discovers frequently occurring item sets in a database and presents the patterns as rules. This technique has been applied in network intrusion detection to derive association rules from users’ interaction history. Investigators also can apply this technique to network intruders’ profiles to help detect potential future network attacks. Similar to association rule mining, sequential pattern mining finds frequently occurring sequences of items over a set of transactions that occurred at different times. In network intrusion detection, this approach can identify intrusion patterns among time-stamped data. Showing hidden patterns benefits crime analysis, but to obtain meaningful results requires rich and highly structured data.