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Evaluation of Data Mining Methods for Nigerian Telecommunications Companies A Case Study of MTN Nigeria
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ACKNOWLEDGEMENT

I owe my indebtedness to my Supervisor (Name of your Supervisor), the Head of Department (Name of your HOD), the Lecturers in the department of Computer Science Education, Book Authors and Profound Scholars of existing/related research work for your moral support that facilitated the successful completion of my (Tertiary Institution level). I am grateful to God Almighty and my parent for their financial support in my career. I really appreciate you all for everything, Thank you very much.



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Evaluation of Data Mining Methods for Nigerian Telecommunications Companies (A Case Study of MTN Nigeria)

TABLE OF CONTENTS

Preliminary Pages

Chapter One

Introduction

  • 1.1 Background …

Chapter Two

Literature Review

  • 2.1 Introduction

Chapter Three

Research Methodology

  • 3.1 Introduction

Chapter Four

Results and Discussion

  • 4.1 Introduction

Chapter Five

Summary, Conclusion, and Recommendation

  • 5.1 Introduction
  • 5.2 Summary
  • 5.3 Conclusion
  • 5.4 Recommendation

References




iii


Evaluation of Data Mining Methods for Nigerian Telecommunications Companies (A Case Study of MTN Nigeria)

CHAPTER ONE

Introduction

1.1 Background To The Study

The telecommunications industry generates and stores a tremendous amount of data (Han et al, 2002). 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 (Roset et al, 1999). 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 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 (Roset et al, 1999).

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 data is often not suitable for data mining. For example, both call detail and network data are time-series data that represent individual events. Before this data can be effectively mined, useful “summary” features must be identified and then the data must be summarized using these features. Because many data mining applications in the telecommunications industry involve predicting very rare events, such as the failure of a network element or an instance of telephone fraud, rarity is another issue that must be dealt with. The fourth and final data mining issue concerns real-time performance because many data mining applications, such as fraud detection, require that any learned model/rules be applied in real-time (Ezawa & Norton, 1995). Several techniques has also been applied is tackling all these issues in telecommunication companies.

Telecommunication networks are extremely complex configurations of equipment, comprised of thousands of interconnected components. Each network element is capable of generating error and status messages, which leads to a tremendous amount of network data. This data must be stored and 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. For example, such a message might indicate that “controller 7 experienced a loss of power for 30 seconds starting at 10:03 pm on Monday, May 12.”

Due to the enormous number of network messages generated, technicians cannot possibly handle every message. For this reason expert systems have been developed to automatically analyze these messages and take appropriate action, only involving a technician when a problem cannot be automatically resolved (Weiss, Ros & Singhal, 1998).

This study is focused on MTN Nigeria.

MTN Nigeria is part of the MTN Group, Africa’s leading cellular telecommunications company. On May 16, 2001, MTN became the first GSM network to make a call following the globally lauded Nigerian GSM auction conducted by the Nigerian Communications Commission earlier in the year. Thereafter the company launched full commercial operations beginning with Lagos, Abuja and Port Harcourt. MTN paid $285m for one of four GSM licenses in Nigeria in January 2001. To date, in excess of US$1.8 billion has been invested building mobile telecommunications infrastructure in Nigeria.

Since launch in August 2001, MTN has steadily deployed its services across Nigeria. It now provides services in 223 cities and towns, more than 10,000 villages and communities and a growing number of highways across the country, spanning the 36 states of the Nigeria and the Federal Capital Territory, Abuja. Many of these villages and communities are being connected to the world of telecommunications for the first time ever.

1.2 Statement Of The Problem

Fraud is a serious problem for telecommunication companies, leading to billions of dollars in lost revenue each year. Fraud can be divided into two categories: subscription fraud and superimposition fraud. Subscription fraud occurs when a customer opens an account with the intention of never paying for the account charges. Superimposition fraud involves a legitimate account with some legitimate activity, but also includes some “superimposed” illegitimate activity by a person other than the account holder. Superimposition fraud poses a bigger problem for the telecommunications industry and for this reason data mining technique is used for identifying this type of fraud. These applications should ideally operate in real-time using the call detail records and, once fraud is detected or suspected, should trigger some action. This action may be to immediately block the call and/or deactivate the account, or may involve opening an investigation, which will result in a call to the customer to verify the legitimacy of the account activity. However, this study will examine various data mining techniques of telecommunication companies in Nigeria.

1.3 Objectives Of The Study

The following are the objectives of this study:

  1. To provide an overview on data mining.
  2. To examine the various data mining techniques of telecommunication companies in Nigeria
  3. To identify the challenges of data mining faced by telecommunication companies in Nigeria

1.4 Research Questions

  1. What is data mining?
  2. What are the various data mining techniques of telecommunication companies in Nigeria?
  3. What are the challenges of data mining faced by telecommunication companies in Nigeria?

1.6 Significance Of The Study

The following are the significance of this study:

The outcome of this study will educate on data mining techniques of telecommunication companies in Nigeria, the data mining applications and how they can be used in fraud detection.

This research will be a contribution to the body of literature in the area of the effect of personality trait on student’s academic performance, thereby constituting the empirical literature for future research in the subject area.

1.7 Scope/Limitations Of The Study

This study will cover various data mining techniques used by telecommunication companies in Nigeria.

Limitation Of Study
Financial constraint

Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet, questionnaire and interview).

Time constraint

The researcher will simultaneously engage in this study with other academic work. This consequently will cut down on the time devoted for the research work.

CHAPTER TWO

2.0 Literature Review

2.1 Introduction

This chapter focuses on the review of related literature. A literature review includes the current knowledge as well as theoretical and methodological contributions to a particular topic. It documents the state of the art with respect to the topic you are writing. It surveys the literature in the topic selected. In this research work the literature review includes the …

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  • Brief Background of Study

  • Statement of Problems

  • Aim of the Study

  • Specific Objectives of the Study

  • Significance of the Study (Who benefits from the project and how?)

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Evaluation of Data Mining Methods for Nigerian Telecommunications Companies is a proposal topic for final year research work, which comprises the major and elective project proposal writing sections for Evaluation of Data Mining Methods for Nigerian Telecommunications Companies (A Case Study of MTN Nigeria) research work.

Major Sections
  • Motivation for Embarking on the Project

  • Brief Background of Study

  • Statement of Problems

  • Aim of the Study

  • Specific Objectives of the Study

  • Significance of the Study (Who benefits from the project and how?)

Elective Sections
  • Relevant Research Questions

  • Relevant Research Hypotheses


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CHAPTER ONE

  • Motivation / Statement of Problems
  • Aims & Objective of Study
  • Scope of Study
  • Significance of Study

CHAPTER TWO

  • State two or more citation from your review of related literature.

CHAPTER THREE

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CHAPTER FOUR

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CHAPTER FIVE

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