odels. In order to effectively mine the call detail data, it must be summarized to the customer level as described earlier in this chapter. Then, a classifier induction program can be applied to a set of labeled training examples in order to build a classifier. This approach has been used to identify fax lines (Kaplan, Strauss & Szegedy, 1999) and to classify a phone line as belonging to a business or residence (Cortes & Pregibon, 1998). Other applications have used this approach to identify phone lines belonging to telemarketers and to classify a phone line as being used for voice, data, or fax. Two sample rules for classifying a customer as being a business or residential customer are shown below (using pseudo-code). These rules were generated using SAS Enterprise Miner, a sophisticated data mining package that supports multiple data mining techniques. The rules shown below were generated using a decision tree learner. However, a neural network was also used to predict the probability of a customer being a business or residential customer, based solely on the distribution of calls by time of day (i.e., the neural network had 24 inputs, one per hour of the day). The probability estimate generated by the neural network was then used as an input (i.e., feature) to the decision tree learner. Evaluation on a separate test set indicates that rule 1 is 88% accurate and rule 2 is 70% accurate. Rule 1: if < 43% of calls last 0-10 seconds and < 13.5% of calls occur during the weekend and neural network says that P(business) > 0.58 based on time of day call distribution then business customer Rule 2: if calls received over two-month period from at most 3 unique area codes and <56.6% of calls last 0-10 seconds then residential customer It is worth noting that because a telecommunications company generates a call detail record if the calling (paying) party is its customer, the company will also have a sample of (received) calls for non-customers. If a company has high overall market penetration, this sample may be large eno