Data Mining in Telecommunication


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This presentation describes how Data Mining is used Telecommunication industry.


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Data Mining in Telecommunications:

Data Mining in Telecommunications Prepared by – Mohsin Nadaf, BE IT University of Pune


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


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

What is Data Mining?:

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

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Technical approaches like Clustering, Data summarization Classification Analyzing Changes Detecting anomalies

Data Mining in Telecommunications:

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?

Customer Segmentation and Profiling:

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

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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

Types of Telecommunication Data:

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

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

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

Data Preparation and Clustering:

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

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


Applications Marketing/Customer Profiling Fraud Detection Network Fault Isolation

Future Trends:

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


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


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.

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