form well when the target event is rare, which is critical since most network failures are rare. In the case studied, the target event is the failure of components in the 4ESS switching system. CONCLUSION This chapter described how data mining is used in the telecommunications industry. Three main sources of telecommunication data (call detail, network and customer data) were described, as were common data mining applications (fraud, marketing and network fault isolation). This chapter also highlighted several key issues that affect the ability to mine data, and commented on how they impact the data mining process. One central issue is that telecommunication data is often not in a form-or at a level-suitable for data mining. Other data mining issues that were discussed include the large scale of telecommunication data sets, the need to identify very rare events (e.g., fraud and equipment failures) and the need to operate in real- time (e.g., fraud detection). Data mining applications must always consider privacy issues. This is especially true in the telecommunications industry, since telecommunication companies maintain highly private information, such as whom each customer calls. Most telecommunication companies utilize this information conscientiously and consequently privacy concerns have thus far been minimized. A more significant issue in the telecommunications industry relates to specific legal restrictions on how data may be used. In the United States, the information that a telecommunications company acquires about their subscribers is referred to as Customer Proprietary Network Information (CPNI) and there are specific restrictions on how this data may be used. The Chapter Telecommunications Act of 1996, along with more recent clarifications from the Federal Communications Commission, generally prohibits the use of that information without customer permission, even for the purpose of marketing the customers other services. In the case of customers who switch to other service providers, the original serv