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. In the neural network phase, all the attributes and features of a case are grouped according to their given values (similarity) and each group is then presented to an independent Kohonen network. The independent Kohonen networks provide output independently and in parallel with the other independent networks in the system. Once presented to the independent Kohonen networks, the groups are then combined for a final time and sent to a final Kohonen network. After being sent to the final Kohnen