tion relied on data mining to identify associations within data. Another marketing application that relies on this technique is a data mining application for finding the set of non-U.S. countries most often called together by U.S. telecommunication customers (Cortes & Pregibon, 2001). One set of countries identified by this data mining application is: {Jamaica, Antigua, Grenada, Dominica}. This information is useful for establishing and marketing international calling plans. A serious issue with telecommunication companies is customer churn. Customer churn involves a customer leaving one telecommunication company for another. Customer churn is a significant problem because of the associated loss of revenue and the high cost of attracting new customers. Some of the worst cases of customer churn occurred several years ago when competing long distance companies offered special incentives, typically $50 or $100, for signing up with their company-a practice which led to customers repeatedly switching carriers in order to earn the incentives. Data mining techniques now permit companies the ability to mine historical data in order to predict when a customer is likely to leave. These techniques typically utilize billing data, call detail data, subscription information (calling plan, features, contract expiration data) and customer information (e.g., age). Based on the induced model, the company can then take action, if desired. For example, a wireless company might offer a customer a free phone for extending their contract. One such effort utilized a neural network to estimate the probability h(t) of cancellation at a given time t in the future (Mani, Drew, Betz & Datta, 1999). Data Mining In Telecommunications In the telecommunications industry, it is often useful to profile customers based on their patterns of phone usage, which can be extracted from the call detail data. These customer profiles can then be used for marketing purposes, or to better understand the customer, which in turn may lead to better forecasting m