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Data Mining In Telecommunications 5 analyzed in order to support network management functions, such as fault isolation. This data will minimally include a timestamp, a string that uniquely identifies the hardware or software component generating the message and a code that explains why the message is being generated.
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- Objectives:
- 24.1 Data Mining for the Telecommunication Industry
- 24.1.1 Multidimensional Analysis of Telecommunication Data
- 24.1.2 Fraudulent Pattern Analysis and the Identification of Unusual Patterns
- 24.1.4 Use of Visualization Tools in Telecommunication Data Analysis
- 24.2 Data Mining Focus Areas in Telecommunication
- 24.2.1 Systematic Error
- 24.2.2 Data Mining in Churn Analysis
- Data Mining may be Used in Churn Analysis to Perform Two Key Tasks
- 24.3 A Learning System for Decision Support in Telecommunications – Case Study
- 24.4 Knowledge Processing in Control Systems
- 24.4.1 Preliminaries and General Definitions
- 24.6 Summary
- 24.7 Review Questions
Data mining in telecommunication industry helps to understand the busi-ness involved, identify telecommunication patterns, catch fraudulent ac-tivities, make better use of resources, and improve the quality of service. A large class of data mining algorithms developed for this purpose includes • CART, C4.5, neural networks, and Bayesian classifiers...
The telecommunication industry has quickly evolved from offering local and long-distance telephone services to providing many other comprehensive com-munication services, including voice, fax, pager, cellular phone, images, e-mail, computer, and Web data transmission, and data traffic. The integration of telecommunication, computing network, Intern...
Telecommunication data are intrinsically with dimensions such as calling time, duration, location of caller, and type of call. The multidimensional analysis of such data can be used to identify and compare the data traffic, system work load, resource usage, user group behavior, profit, and so on. For exam-ple, an analyst in the industry may wish to...
Fraudulent activity costs the telecommunication industry millions of dollars a year. It is important to identify potentially fraudulent users and their atyp-ical usage patterns; detect attempts to gain fraudulent entry to customer ac-counts; and discover unusual patterns that may need special attention, such as busy-hour, frustrated call attempts, ...
Tools for OLAP visualization, linkage visualization, association visualization, clustering, and outlier visualization have been shown to be very useful for telecommunication data analysis.
The experience with applications of interest to the telecommunications busi-ness was carried out at Bell Atlantic STC, NY, where we focus on machine learning algorithms. A large class of data mining algorithms has developed out of ideas investigated earlier by researchers and developers of machine learn-ing algorithms. Notable examples include CART...
Systematic errors arise in many applications, and they may be due to any of the following: Errors of calibration of instruments. Personal errors. These are errors caused by habits of individual observes. Imperfect technique. We have found many examples of these in some of the telecommunications applications, investigated at Bell Atlantic Science an...
Data mining is the discovery of knowledge from data, and uses a variety of tools ranging from classical statistical methods to neural networks and other new techniques originating from machine learning and artificial intel-ligence. Recently, data mining has been used with substantial results in en-abling and improving database marketing, process op...
Predict whether a particular customer will churn and when it will happen; Understand why particular customers churn. These prediction and understanding tasks represent the two most impor-tant aspects of data mining in use today. By predicting which customers are likely to churn, the company can reduce the rate of churn by offering cus-tomers new in...
We present a system for decision support in telecommunications. History data describing the operation of a telephone exchange is analyzed by the system to reconstruct understandable event descriptions. The event descriptions are processed by an algorithm inducing rules describing regularities in the events. The rules can be used as decision support...
Several large real-time applications are required to operate in environments that are not fully structured. The lack of information and uncertainty of the environment requires the use of problems-solving techniques. Elevator group control is one such application. There are many possible situations compris-ing the state of all elevators, existing ca...
A typical RTKP system acting as a direct digital control system is shown in Fig. 24.1. The RTKP module is connected to information sources and re-ceivers. Sources may be sensors connected to a process, human users, or even computer programs in large integrated systems. Receivers can be either actu-ators, human users, or again computer programs. The...
This section has described data mining applications in telecommunications industry, and a learning system for decision support in telecommunications case study, knowledge processing in control systems and aircraft control case study. Data mining can be applied fruitfully, as in network capacity utilization. In network capacity utilization, planning...
How can data mining improve telecommunication services? Write a short note on systematic error observed in mining. How is data mining used in churn analysis? Explain how data mining is used in PBX areas. With typical structure explain real-time knowledge processing (RTKP) in direct digital control and supervisory control systems.
The fourth and final Data Mining issue concerns real-time performance: many Data Mining applications, such as fraud detection, require that any learned modeYrules be applied in real-time. Each of these four issues are discussed throughout this chapter, within the context of real Data Mining applications. 2. Qpes of Telecommunication Data
- Gary M. Weiss
- 2005
The main purpose of this paper is to present a literature review related to BI and data Mining in Telecommunications, from business perspective defining the main areas of BI and Data Mining applications, and from research perspective identifying the most common Data Mining techniques and methods used. Expand. 3.
& Weiss, 1996) before mining the data. An alternative is to utilize a data mining method that can operate on the transactional data directly and extract sequential or temporal patterns (Klemettinen, Mannila & Toivonen, 1999; Weiss & Hirsh, 1998). Another issue arises because much of the telecom-munications data is generated in real-time and many
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n the early 1990s. mining, alsoe - ISSN 1857- 7431known as knowledge discovery databases (KDD), is the process of extracting useful patterns and knowl. dge from large amounts of data. It may also be regarded as the process of analyzing data from different perspective and summa. izing it into useful information. It is an interdiscipli.
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Data Mining and Data Warehousing This textbook is written to cater to the needs of u ndergraduate students of computer science, engineer ing, and information technology for a course on data minin g and data warehousing. It brings together fundamental concepts of data mining and data wareho using in a single volume. Important topics includin g
