 data is often not suitable for data mining. For example, both call detail and network data are time-series data that represent individual events. Before this data can be effectively mined, useful "summary" features must be identified and then the data must be summarized using these features. Because many data mining applications in the telecommunications industry involve predicting very rare events, such as the failure of a network element or an instance of telephone fraud, rarity is another issue that must be dealt with. The fourth and final data mining issue concerns real-time performance: many data mining applications, such as fraud detection, require that any learned model/rules be applied in real-time. Each of these four issues are discussed throughout this chapter, within the context of real data mining applications. TYPES OF TELECOMMUNICATION DATA The first step in the data mining process is to understand the data. Without such an understanding, useful applications cannot be developed. In this section we describe the three main types of telecommunication data. If the raw data is not suitable for data mining, then the transformation steps necessary to generate data that can be mined are also described. Section 3 will show how data mining can be used to extract useful information from these data sets. Call Detail Data Every time a call is placed on a telecommunications network, descriptive information about the call is saved as a call detail record. The number of call detail records that are generated and stored is huge. For example, AT&T long distance customers alone generate over 300 million call detail records per day (Cortes & Pregibon, 2001). Given that several months of call detail data is typically kept online, this means that tens of billions of call detail records will need to be stored at any time. Call detail records include sufficient information to describe the important characteristics of each call. At a minimum, each call detail record will include the originating and terminating phone number