The Application of Data Mining in Telecommunication by Wang Lina February 2003

Telecommunication Today Market is rapidly expanding Intense competitions locally and globally New computer and communication technologies Integration of telecommunication with computer network, Internet, and numerous other means of communication and computing

Telecommunication Today Amount of data collected and warehoused is growing phenomenally Companies increasingly rely on analysis of huge amounts of data to compete in the market Why? It helps to understand the business involved Identify telecommunication pattern Catch fraudulent activities Make better use of resources And improve the quality of services

Telecommunication Today “Three strategic options ” (Mattison, 1997) --- for telecommunication firms Customer Intimacy --- from “network is king” to “customer is king” Operational Efficiency ---being the low-cost provider of choice Technical Proficiency --- being the best at what you do

Telecommunication Today Inter-relating the three options  Better utilize its resources Subsequently improve its profitability and competitiveness Basic functions + Appropriate information systems  Eventually facilitate a better understanding of the company’s knowledge infrastructure and potential knowledge management needs.

Valuable Data Mining Approach Data Intensity Being one of the most data-intensive industries There are thousands of calls or connection transactions These transactions must be executed and kept track of accordingly. Analysis Dependency No tangible goods to track How well they run the business is incredibly dependent on the ability to strive from tons of raw, abstract data And how they can make intelligent decisions based on what they analyze and extract from the data

Valuable Data Mining Approach Competitive Climate No longer monopoly and government regulated A change from serious lack of foresight to a better alertness of how to create the technological infrastructures and organizational cultural preferences Data mining techniques may help companies develop new marketing-based infrastructures quickly And empower organizations to make the necessary cultural changes to transform them to be more customer-centric and less technology-centric.

Valuable Data Mining Approach Technologies Change at a High Rate Constantly re-evaluate their investment in infrastructure and design of networks. Adjust themselves to keep pace with related technology changes Analyze how well the changes are being implemented. Data mining approach can respond flexibly and instantly to those unpredictable and chaotic changes. Historical Precedent A long and rich history of making use of data management innovations to their advantage Will also be an ideal starting point for the development of a powerful mining organization

Applications of Data Mining Customer oriented marketing Customer intimacy Predicting customer behavior Business modeling and decision support Eg. Construct a scoring model for adopters of a new telecommunication service to identify the general characteristics that may influence the adoption of the new service and to forecast the probability of adoption at the individual prospect level.

Applications of Data Mining Operational monitoring and infrastructural control Alarm prediction and alarm control Detection of computer and network system intrusion Eg. The alarm data of the GSM systems will be cleaned and transformed before the actual mining procedure. Subsequently, with proper counting methods and algorithms, the occurrence count and discovery of the sequential alarm patterns in accordance with the nature of alarms can be determined.

Applications of Data Mining Technical proficiency Engineering and competitive analysis support Optimization of mobile network Optimization of traffic throughout the networks Eg. Mobile network technology changes too quickly, a new knowledge extracting method has to be adopted, that is, KDD. With an expert system that uses the knowledge of expert to solve the problems in some special field and utilizes KDD to update the knowledge with the main parts of the knowledge base, the inferring machine, and data handling.

Conclusion Among one of the most popular application domains for data mining. Most of the application followed a typical KDD framework. Domain knowledge was recognized to play a notable role that was not easy to evaluate in exact terms. Data mining techniques have been assessed to be valuable and effective in helping the network operators to make decisions.

Conclusion Various algorithms will be tested to establish the most suitable model for a specific problem and a particular dataset. With similar characteristics, the findings of these application examples are still applicable to a wider part of the telecommunication industry. ~ The End ~