ugh for data mining. Thus, telecommunication companies have the technical ability to profile non-customers as well as customers. However, there are legal restrictions on the use of this data, some of which are described in Section 4. Chapter Network Fault Isolation Telecommunication networks are extremely complex configurations of hardware and software. Most of the network elements are capable of at least limited self-diagnosis, and these elements may collectively generate millions of status and alarm messages each month. In order to effectively manage the network, alarms must be analyzed automatically in order to identify network faults in a timely manner-or before they occur and degrade network performance. A proactive response is essential to maintaining the reliability of the network. Because of the volume of the data, and because a single fault may cause many different, seemingly unrelated, alarms to be generated, the task of network fault isolation is quite difficult. Data mining has a role to play in generating rules for identifying faults. The Telecommunication Alarm Sequence Analyzer (TASA) is one tool that helps with the knowledge acquisition task for alarm correlation (Klemettinen, Mannila & Toivonen, 1999). This tool automatically discovers recurrent patterns of alarms within the network data along with their statistical properties, using a specialized data mining algorithm. Network specialists then use this information to construct a rule-based alarm correlation system, which can then be used in real-time to identify faults. TASA is capable of finding episodic rules that depend on temporal relationships between the alarms. For example, it may discover the following rule: "if alarms of type link alarm and link failure occur within 5 seconds, then an alarm of type high fault rate occurs within 60 seconds with probability 0.7." Before standard classification tasks can be applied to the problem of network fault isolation, the underlying time-series data must be re- represented as a set of classified exam