To construct a model for detecting perpetrators using an MLP neural network, samples of criminal observations and their attributes are required. It is through the training of these samples that connections among the various layers in the network can be made to converge. This enables a model to be created for recognizing future perpetrators, so that whenever the features of a criminal are input, an alert output is produced, since a network will associate and recognize a profile of similarity.
The neural network is then said to have learned to recognize the features of criminal perpetrators. Once training is complete, the neural network uses these learned patterns to predict the probability that a new individual will exhibit the modeled transaction patterns. Moreover, the trained network is able to recognize new perpetrators that are similar, but not identical to, those used in its training sessions.
This application of training and modeling for profiling and prediction to investigative data mining can be applied in criminal investigations, fraud detection, internal corporate investigations, cybercrimes, system intrusion detection, criminal profiling, and criminal analysis and prevention. The challenge, as some of the case studies will illustrate, is in the encoding of the data. Most law enforcement departments and government agencies have not adequately digitized their criminal records into a structured and uniform manner. Many still use free-form text narrative entries in documenting their criminal cases; this impedes the use of neural-network techniques. The top law enforcement agency in the United States, the FBI, has made great strides in gathering criminal data for its investigations and for reports; however, by its own admission, it has been remiss in its analysis of this data.
If a standardized system of recording crimes can be developed, data mining techniques can begin to be deployed in order for crime to be analyzed, modeled, and to an extent predicted. For this to happen, effort on the parts of the local, state, and federal law enforcement agencies must begin to structure in a machine-readable format how crimes get reported and documented. Once the crime data is standardized and warehoused, it becomes a much easier task to analyze it to extract predictive models from it.