s, the date and time of Data Mining In Telecommunications the call and the duration of the call. Call detail records are generated in real- time and therefore will be available almost immediately for data mining. This can be contrasted with billing data, which is typically made available only once per month. Call detail records are not used directly for data mining, since the goal of data mining applications is to extract knowledge at the customer level, not at the level of individual phone calls. Thus, the call detail records associated with a customer must be summarized into a single record that describes the customer's calling behavior. The choice of summary variables (i.e., features) is critical in order to obtain a useful description of the customer. Below is a list of features that one might use when generating a summary description of a customer based on the calls they originate and receive over some time period P: 1. average call duration 2. % no-answer calls 3. % calls to/from a different area code 4. % of weekday calls (Monday - Friday) 5. % of daytime calls (9am - 5pm) 6. average # calls received per day 7. average # calls originated per day 8. # unique area codes called during P These eight features can be used to build a customer profile. Such a profile has many potential applications. For example, it could be used to distinguish between business and residential customers based on the percentage of weekday and daytime calls. Most of the eight features listed above were generated in a straightforward manner from the underlying data, but some features, such as the eighth feature, required a little more thought and creativity. Because most people call only a few area codes over a reasonably short period of time (e.g., a month), this feature can help identify telemarketers, or telemarketing behavior, since telemarketers will call many different area codes. The above example demonstrates that generating useful features, including summary features, is a critical step within the data mining process. Should 