poor features be generated, data mining will not be successful. Although the construction of these features may be guided by common sense and expert knowledge, it should include exploratory data analysis. For example, the use of the time period 9am-5pm in the fifth feature is based on the commonsense knowledge that the typical workday is 9 to 5 (and hence this feature may be useful in distinguishing between business and residential calling patterns). However, more detailed exploratory data analysis, shown in Figure 1, indicates that the period from 9am to 4pm is actually more Chapter appropriate for this purpose. Figure 1 plots, for each weekday hour, h, the business to residential call ratio, which is computed as: % weekday business calls during h/ % weekday residential calls during h Thus, this figure shows that during the period of 9am to 4pm, businesses place roughly 1.5 times as many of their total weekday calls as does a residence. Note that at 5pm the ratio is close to 1, indicating that the calls during this timeframe are not very useful for distinguishing between a business and a residence. However, calls in the evening timeframe (6pm - 1am) are also useful in distinguishing between the two types of customers. Daytime Evening (9am 4pm) (6pm 1 am) 9 10 11 12123456 Hour of Day Figure - 1 Comparison of Business and Residential Hourly Calling Patterns For some applications, such as fraud detection, the summary descriptions, sometimes called signatures (Cortes & Pregibon, 2001), must be updated in real-time for millions of phone lines. This requires the use of fairly short and simple summary features that can be updated quickly and efficiently. Network Data Telecommunication networks are extremely complex configurations of equipment, comprised of thousands of interconnected components. Each network element is capable of generating error and status messages, which leads to a tremendous amount of network data. This data must be stored and Data Mining In Telecommunications analyzed in order to support network ma