Data Mining For The Telecommunication Industry

Introduction: The telecommunication industry has quickly evolved from offering local and long distance telephone services to providing many other comprehensive communication services, including fax, pager, cellular phone, Internet messenger, images, e-mail, computer and Web data transmission, and other data traffic. The integration of telecommunication, computer network, Internet, and numerous other means of communication and computing is also underway. Moreover, with the deregulation of the telecommunication industry in many countries and the development of new computer and communication technologies, the telecommunication market is rapidly expanding and highly competitive.

This creates a great demand for data mining in order to help understand the business involved, identify telecommunication patterns, catch fraudulent activities, make better use of resources, and improve the quality of service.

The following are a few scenarios for which data mining may improve telecommunication services:

Multidimensional analysis of telecommunication data: Telecommunication data are intrinsically multidimensional, with dimensions such as calling-time, duration, location of caller, location of call ee, and type of call. The multidimensional analysis of such data can be used to identify and compare the data traffic, system workload, resource usage, user group behavior, and profit. For example, analysts in the industry may wish to regularly view charts and graphs regarding calling source, destination, volume, and time-of-day usage patterns. Therefore, it is often useful to consolidate telecommunication data into large data warehouses and routinely perform multidimensional analysis using OLAP and visualization tools.

Fraudulent pattern analysis and the identification of unusual patterns: Fraudulent activity costs the telecommunication industry millions of dollars per year. It is important to (1) identify potentially fraudulent users and their atypical usage patterns; (2) detect attempts to gain fraudulent entry to customer accounts; and (3) discover unusual patterns that may need special attention, such as busy-hour frustrated call attempts, switch and route congestion patterns, and periodic calls from automatic dial-out equipment (like fax machines) that have been improperly programmed. Many of these patterns can be discovered by multidimensional analysis, cluster analysis, and outlier analysis.

Multidimensional association and sequential pattern analysis: The discovery of association and sequential patterns in multidimensional analysis can be used to promote telecommunication services. For example, suppose you would like to find usage patterns for a set of communication services by customer group, by month, and by time of day. The calling records may be grouped by customer in the following form:

{customer_ID; residence, office, time, date, service_1, service_2, . . . . . }

A sequential pattern like “If a customer in the Los Angeles area works in a city different from her residence, she is likely to first use long-distance service between two cities around 5 p.m. and then use a cellular phone for at least 30 minutes in the subsequent hour every weekday” can be further probed by drilling up and down in order to determine whether it holds for particular pairs of cities and particular groups of persons (e.g., engineers, doctors). This can help promote the sales of specific long-distance and cellular phone combinations and improve the availability of particular services in the region.

Mobile telecommunication services: Mobile telecommunication, Web and information services, and mobile computing are becoming increasingly integrated and common in our work and life. One important feature of mobile telecommunication data is its association with spatiotemporal information. Spatiotemporal data mining may become essential for finding certain patterns. For example, unusually busy mobile phone traffic at certain locations may indicate something abnormal happening in these locations. Moreover, ease of use is crucial for enticing customers to adopt new mobile services. Data mining will likely play a major role in the design of adaptive solutions enabling users to obtain useful information with relatively few keystrokes.

Use of visualization tools in telecommunication data analysis: Tools for OLAP visualization, linkage visualization, association visualization, clustering, and outlier visualization have been shown to be very useful for telecommunication data analysis.