1. Prepared by –
Mohsin Nadaf, TE IT
Trinity College of Engineering & Research, Pune

2. Contents
 Introduction
 What is Data Mining?
 Need of Data mining in Telecommunication
 Customer Segmentation and Profiling
 Types of Telecommunication Data
 Data Preparation and Clustering
 Applications
 Conclusion

3. Introduction
 Fast growing Industry
 Data, the base of Telecommunication
 Generation of tremendous amount of Data
 Knowledge based Expert-System
 Use of Data Mining and its tools
 Uncovering hidden information
 Future Decisions

4. What is Data Mining?
 Extracting Knowledge hidden in large volumes of data
 Identifying potentially useful and understandable data


5.  Technical approaches like
 Clustering,
 Data summarization
 Classification
 Analyzing Changes
 Detecting anomalies

6. Data Mining in
Telecommunications
 To detect frauds
 To know customers
 Retain Customers
 What products and services yield highest amount of
profit?
 What are the factors that influence customers to call
more at certain times?

7. Customer Segmentation and
Profiling
 Customer Segmentation
-To describe the process of dividing customers into
homogeneous groups on the basis of shared or
common attributes (habits, tastes, etc).
 Difficulties :
-Relevance and quality of data
-Intuition
-Continuous process
-Over-segmentation

8.  Customer Profiling
-Describing customers by their attributes, such as
age, gender, income and lifestyles
 Parameters-
-Geographic
-Cultural and ethnic
-Economic conditions
-Age and Gender
-Attitudes and beliefs
-Lifestyle
-Knowledge and Awareness

9. Types of Telecommunication Data
 Call-Detail Data
 Network Data
 Customer Data
 Call-Detail Data
-average call duration
-average call originated/generated
-call period
-call to/from different area code

11.  Network Data
-Complex configuration of equipments-
-Error Generation
-To support Network Management functions

12.  Customer Data
-Database of information of Customers
-Name
-Age
-Address
-Telephone type
-Subscription Type
-Payment History

13. Data Preparation and Clustering
 Data preparation
-To be prepared in the required format
 Tasks:
Discovering and Repairing inconsistent data
format
Deleting unwanted data fields
Combining data
Mapping of values
Normalization of the variables

14.  Clustering
-Grouping of Similar things
 Cluster Analysis
-Organization of objects into groups, according to
similarities among them.

15. Applications
 Marketing/Customer Profiling
 Fraud Detection
 Network Fault Isolation

16. CONCLUSION
 Early adopter of Data mining technology
 To detect frauds
 Helps to know the Customer
 Serve them Better
 Yield more profit
 Reduced much of Human based analysis
 Essential for Telecommunication companies

17. Future Trends
 Additional themes on data mining
 New Methods for Complex types of Data
 Invisible Data mining(mining as a built in function)
 Reduction in Human work
 Advanced methods in Data mining

18. REFERENCES
 Data mining in Telecommunication by Gray M. Weiss,
Fordham University
 Customer Segmentation and Customer Profiling for a Mobile
Telecommunications Company Based on Usage
Behaviour, S.M.H Jansen, July 17, 2007
 IJSETT -Applications of Data Mining by Simmi Bagga and Dr.
G.N.Singh
 A new approach to classify and describe telecommunication
services, A.Lehmann1,2, W.Fuhrmann3, U.Trick1, B.Ghita²
 Sasisekharan, R., Seshadri, V., Weiss, S. Data mining and
forecasting in large-scale telecommunication networks.
IEEE Expert 1996; 11(1):37-43.

20.  Liked the presentation? You can download it from my
website:
http://www.thetechworld21.com/2016/04/download-
data-mining-in.html