anies generate a tremendous amount of data. These data include call detail data, which describes the calls that traverse the telecommunication networks, network data, which describes the state of the hardware and software components in the network, and customer data, which describes the telecommunication customers. This chapter describes how data mining can be used to uncover useful information buried within these data sets. Several data mining applications are described and together they demonstrate that data mining can be used to identify telecommunication fraud, improve marketing effectiveness, and identify network faults. Key words: Telecommunications, fraud detection, marketing, network fault isolation. INTRODUCTION The telecommunications industry generates and stores a tremendous amount of data. These data include call detail data, which describes the calls that traverse the telecommunication networks, network data, which describes the state of the hardware and software components in the network, and customer data, which describes the telecommunication customers. The amount of data is SO great that manual analysis of the data is difficult, if not impossible. The need to handle such large volumes of data led to the development of knowledge-based expert systems. These automated systems performed important functions such as identifying fraudulent phone calls and identifying network faults. The problem with this approach is that it is time- consuming to obtain the knowledge from human experts (the "knowledge acquisition bottleneck") and, in many cases, the experts do not have the Chapter requisite knowledge. The advent of data mining technology promised solutions to these problems and for this reason the telecommunications industry was an early adopter of data mining technology. Telecommunication data pose several interesting issues for data mining. The first concerns scale, since telecommunication databases may contain billions of records and are amongst the largest in the world. A second issue is that the raw