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Data Mining in the Telecommunications Industry
Abstract
The telecommunications industry was one of the first to adopt data mining technology. This is most likely because telecommunication companies routinely generate and store enormous amounts of high-quality data, have a very large customer base, and operate in a rapidly changing and highly competitive environment. Telecommunication companies utilize data mining to improve their marketing efforts, identify fraud, and better manage their telecommunication networks. However, these companies also face a number of data mining challenges due to the enormous size of their data sets, the sequential and temporal aspects of their data, and the need to predict very rare events—such as customer fraud and network failures—in real-time. The popularity of data mining in the telecommunications industry can be viewed as an extension of the use of expert systems in the telecommunications industry (Liebowitz, 1988). These systems were developed to address the complexity associated with maintaining a huge network infrastructure and the need to maximize network reliability while minimizing labor costs. The problem with these expert systems is that they are expensive to develop because it is both difficult and timeconsuming to elicit the requisite domain knowledge from experts. Data mining can be viewed as a means of automatically generating some of this knowledge directly from the data.
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