The Classification System for Serial Criminal Patterns (CSSCP) is an artificial intelligence computer system that assists law enforcement officials in identifying links between serial crimes. Working in conjunction with a neural network called a Kohonen network, CSSCP finds patterns in law enforcement databases by analyzing the characteristics of an offender, the criminal activities that have occurred, and the objects used in a crime. Once the links between crimes have been identified by CSSCP, law enforcement officials can then use the data that is produced to build leads or solve criminal cases. Through its capability to run autonomously, the CSSCP has proven that it can operate non-stop without any human interaction and can achieve results with much more accuracy and efficiency than a human.
Background
The Classification System for Serial Criminal Patterns was started by Professor Thomas Muscarello and Professor Kamal Dahbur at DePaul University in Chicago, Illinois in 1996 with the help of a Chicago Police Detective. Muscarello and Dahbur recognized the need for their hybrid system as a result of recognized deficiencies in police practices. These deficiencies were said to be a result of the police's difficulties in analyzing data and transforming it into information that could be useful in the investigation of crimes. In addition to assisting law enforcement officials interpret data, the CSSCP was also designed to help investigators determine which criminal data was critical to an investigation and which format it should be stored in.
Although a similar project was previously undertaken by Timothy O'Shea, Muscarello and Dahbur noticed that problems existed with this proposed system because it relied on limited pre-processing and complex algorithms that lead to computational problems.
The objective of Professor Muscarello and Professor Dahbur was to create an "automated methodology that can systematically identify groups of records as potential patterns for serial criminals, with a good degree of accuracy".
System Design
The CSSCP program was designed to work in three separate phases in conjunction with a Kohonen network. The three phases of CSSCP include:
Pre-processing phase
Neural network phase
Heuristics system phase
Pre-Processing Phase
The Pre-Processing Phase of CSSCP is considered to be the most important phase of the system because it is the phase in which data is structured and put in a format that can be used by the neural network. This phase is where CSSCP will analyze the records it's provided, detect patterns among the data, and assign the data values according to the algorithm chosen. The pre-processing phase relies heavily on accurate and complete input data in order for the output data (results) to also be accurate and complete.
Within the pre-processing phase of CSSCP there are four major functions that take place in order to ensure that input data is processed correctly for the next phases. The four functions include:
Providing categories for missing values- In CSSCP, when a characteristic of a crime comes up that has not been previously assigned a value, the pre-processing phase will ensure that a category is created to account for that value so that it can be grouped with other characteristics of the same value in the future.
Standardization- By standardizing all the values (both discrete and continuous), the data can be better analyzed and a measure can be given to their significance in relation to other values. The standardization of all attributes serves the very important purpose of closing down the gaps among their respective domains.
Categorizations & re-categorization- The Categorizing and re-categorizing of values and variables allows for users of CSSCP to determine a relationship between the values and variables and to create broader categories for data to be classified in. This function of CSSCP was created so that fewer categories could be used and so that the values could have more meaning to the user.
Grouping- Grouping in CSSCP is conducted so that discovering patterns can be done more effectively. Grouping allows adjustments to be made to the variables so that patterns can be depicted.
Neural Network Phase
The Neural Network phase of CSSCP is where machine learning algorithms are used for clustering and prediction tasks. The type of neural network used in CSSCP is called a Kohonen network. The Kohonen network is an unsupervised learning network that uses a simple algorithm to make computations without needing to know the type or number of classifications to be used.