SlideShare a Scribd company logo
Prepared by –
Mohsin Nadaf, TE IT
Trinity College of Engineering & Research, 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?
 Extracting Knowledge hidden in large volumes of data
 Identifying potentially useful and understandable data

 Technical approaches like
 Clustering,
 Data summarization
 Classification
 Analyzing Changes
 Detecting anomalies
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
-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
 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
 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
Data mining in Telecommunications
 Network Data
-Complex configuration of equipments-
-Error Generation
-To support Network Management functions
 Customer Data
-Database of information of Customers
-Name
-Age
-Address
-Telephone type
-Subscription Type
-Payment History
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
 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
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
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
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.
Data mining in Telecommunications
 Liked the presentation? You can download it from my
website:
http://www.thetechworld21.com/2016/04/download-
data-mining-in.html
Data mining in Telecommunications

More Related Content

What's hot

Introduction to Data mining
Introduction to Data miningIntroduction to Data mining
Introduction to Data mining
Hadi Fadlallah
 
Customer churn prediction for telecom data set.
Customer churn prediction for telecom data set.Customer churn prediction for telecom data set.
Customer churn prediction for telecom data set.
Kuldeep Mahani
 
Application of data mining
Application of data miningApplication of data mining
Application of data mining
SHIVANI SONI
 
Data Mining in Retail Industries
Data Mining in Retail IndustriesData Mining in Retail Industries
Data Mining in Retail Industries
Rahul Sinha
 
Web mining
Web mining Web mining
Web mining
TeklayBirhane
 
Data mining PPT
Data mining PPTData mining PPT
Data mining PPT
Kapil Rode
 
Telecom Churn Prediction Presentation
Telecom Churn Prediction PresentationTelecom Churn Prediction Presentation
Telecom Churn Prediction Presentation
PinintiHarishReddy
 
Churn modelling
Churn modellingChurn modelling
Churn modelling
Yogesh Khandelwal
 
Churn prediction
Churn predictionChurn prediction
Churn prediction
Gigi Lino
 
The 8 Step Data Mining Process
The 8 Step Data Mining ProcessThe 8 Step Data Mining Process
The 8 Step Data Mining Process
Marc Berman
 
Mobile Phone Seizure Guide by Raghu Khimani
Mobile Phone Seizure Guide by Raghu KhimaniMobile Phone Seizure Guide by Raghu Khimani
Mobile Phone Seizure Guide by Raghu Khimani
Dr Raghu Khimani
 
Telco churn presentation
Telco churn presentationTelco churn presentation
Telco churn presentation
Aditya Bahl
 
Social Media Forensics
Social Media ForensicsSocial Media Forensics
Social Media Forensics
John J. Carney, Esq.
 
Mobile network structure
Mobile network structure Mobile network structure
Mobile network structure
Ahmed Hussien Ali Gomaa Bebars
 
Data mining (lecture 1 & 2) conecpts and techniques
Data mining (lecture 1 & 2) conecpts and techniquesData mining (lecture 1 & 2) conecpts and techniques
Data mining (lecture 1 & 2) conecpts and techniques
Saif Ullah
 
Data mining slides
Data mining slidesData mining slides
Data mining slides
smj
 
Introduction to Data Mining
Introduction to Data Mining Introduction to Data Mining
Introduction to Data Mining
Sushil Kulkarni
 
Data Preprocessing
Data PreprocessingData Preprocessing
Data Mining Concepts
Data Mining ConceptsData Mining Concepts
Data Mining Concepts
Dung Nguyen
 
Introduction to Data Mining
Introduction to Data MiningIntroduction to Data Mining
Introduction to Data Mining
DataminingTools Inc
 

What's hot (20)

Introduction to Data mining
Introduction to Data miningIntroduction to Data mining
Introduction to Data mining
 
Customer churn prediction for telecom data set.
Customer churn prediction for telecom data set.Customer churn prediction for telecom data set.
Customer churn prediction for telecom data set.
 
Application of data mining
Application of data miningApplication of data mining
Application of data mining
 
Data Mining in Retail Industries
Data Mining in Retail IndustriesData Mining in Retail Industries
Data Mining in Retail Industries
 
Web mining
Web mining Web mining
Web mining
 
Data mining PPT
Data mining PPTData mining PPT
Data mining PPT
 
Telecom Churn Prediction Presentation
Telecom Churn Prediction PresentationTelecom Churn Prediction Presentation
Telecom Churn Prediction Presentation
 
Churn modelling
Churn modellingChurn modelling
Churn modelling
 
Churn prediction
Churn predictionChurn prediction
Churn prediction
 
The 8 Step Data Mining Process
The 8 Step Data Mining ProcessThe 8 Step Data Mining Process
The 8 Step Data Mining Process
 
Mobile Phone Seizure Guide by Raghu Khimani
Mobile Phone Seizure Guide by Raghu KhimaniMobile Phone Seizure Guide by Raghu Khimani
Mobile Phone Seizure Guide by Raghu Khimani
 
Telco churn presentation
Telco churn presentationTelco churn presentation
Telco churn presentation
 
Social Media Forensics
Social Media ForensicsSocial Media Forensics
Social Media Forensics
 
Mobile network structure
Mobile network structure Mobile network structure
Mobile network structure
 
Data mining (lecture 1 & 2) conecpts and techniques
Data mining (lecture 1 & 2) conecpts and techniquesData mining (lecture 1 & 2) conecpts and techniques
Data mining (lecture 1 & 2) conecpts and techniques
 
Data mining slides
Data mining slidesData mining slides
Data mining slides
 
Introduction to Data Mining
Introduction to Data Mining Introduction to Data Mining
Introduction to Data Mining
 
Data Preprocessing
Data PreprocessingData Preprocessing
Data Preprocessing
 
Data Mining Concepts
Data Mining ConceptsData Mining Concepts
Data Mining Concepts
 
Introduction to Data Mining
Introduction to Data MiningIntroduction to Data Mining
Introduction to Data Mining
 

Similar to Data mining in Telecommunications

Data mining and its applications!
Data mining and its applications!Data mining and its applications!
Data mining and its applications!
COSTARCH Analytical Consulting (P) Ltd.
 
Data mining
Data miningData mining
Data mining
sagar dl
 
Data mining
Data miningData mining
Data mining
Ahmed Moussa
 
Aanlytics on Telecom
Aanlytics on TelecomAanlytics on Telecom
Data mining and privacy preserving in data mining
Data mining and privacy preserving in data miningData mining and privacy preserving in data mining
Data mining and privacy preserving in data mining
Needa Multani
 
Data Mining Xuequn Shang NorthWestern Polytechnical University
Data Mining Xuequn Shang NorthWestern Polytechnical UniversityData Mining Xuequn Shang NorthWestern Polytechnical University
Data Mining Xuequn Shang NorthWestern Polytechnical University
butest
 
Data Mining in Telecommunication Industry
Data Mining in Telecommunication IndustryData Mining in Telecommunication Industry
Data Mining in Telecommunication Industry
ijsrd.com
 
1. What are the business costs or risks of poor data quality Sup.docx
1.  What are the business costs or risks of poor data quality Sup.docx1.  What are the business costs or risks of poor data quality Sup.docx
1. What are the business costs or risks of poor data quality Sup.docx
SONU61709
 
Big data analytics for telecom operators final use cases 0712-2014_prof_m erdas
Big data analytics for telecom operators final use cases 0712-2014_prof_m erdasBig data analytics for telecom operators final use cases 0712-2014_prof_m erdas
Big data analytics for telecom operators final use cases 0712-2014_prof_m erdas
Prof Dr Mehmed ERDAS
 
Big data analytics for telecom operators final use cases 0712-2014_prof_m erdas
Big data analytics for telecom operators final use cases 0712-2014_prof_m erdasBig data analytics for telecom operators final use cases 0712-2014_prof_m erdas
Big data analytics for telecom operators final use cases 0712-2014_prof_m erdas
Prof Dr Mehmed ERDAS
 
Data Mining
Data MiningData Mining
Data Mining
AbinayaJawahar
 
data analytics lecture2.pptx
data analytics lecture2.pptxdata analytics lecture2.pptx
data analytics lecture2.pptx
NamrataBhatt8
 
Capturing Marketing Information to Fuel Growth
Capturing Marketing Information to Fuel GrowthCapturing Marketing Information to Fuel Growth
Capturing Marketing Information to Fuel Growth
American Marketing Association | Journals
 
Statistika dan Analisis Data
Statistika dan Analisis DataStatistika dan Analisis Data
Statistika dan Analisis Data
kisti purwitosari
 
Data Mining
Data MiningData Mining
Data Mining
SOMASUNDARAM T
 
1. Introduction to Data Mining (12).pptx
1. Introduction to Data Mining (12).pptx1. Introduction to Data Mining (12).pptx
1. Introduction to Data Mining (12).pptx
Kiran119578
 
Data mining-basic
Data mining-basicData mining-basic
Data mining-basic
gufranresearcher
 
Social Network Analysis
Social Network AnalysisSocial Network Analysis
Social Network Analysis
George Krasadakis
 
Data mining final year project in jalandhar
Data mining final year project in jalandharData mining final year project in jalandhar
Data mining final year project in jalandhar
deepikakaler1
 
Data mining final year project in ludhiana
Data mining final year project in ludhianaData mining final year project in ludhiana
Data mining final year project in ludhiana
deepikakaler1
 

Similar to Data mining in Telecommunications (20)

Data mining and its applications!
Data mining and its applications!Data mining and its applications!
Data mining and its applications!
 
Data mining
Data miningData mining
Data mining
 
Data mining
Data miningData mining
Data mining
 
Aanlytics on Telecom
Aanlytics on TelecomAanlytics on Telecom
Aanlytics on Telecom
 
Data mining and privacy preserving in data mining
Data mining and privacy preserving in data miningData mining and privacy preserving in data mining
Data mining and privacy preserving in data mining
 
Data Mining Xuequn Shang NorthWestern Polytechnical University
Data Mining Xuequn Shang NorthWestern Polytechnical UniversityData Mining Xuequn Shang NorthWestern Polytechnical University
Data Mining Xuequn Shang NorthWestern Polytechnical University
 
Data Mining in Telecommunication Industry
Data Mining in Telecommunication IndustryData Mining in Telecommunication Industry
Data Mining in Telecommunication Industry
 
1. What are the business costs or risks of poor data quality Sup.docx
1.  What are the business costs or risks of poor data quality Sup.docx1.  What are the business costs or risks of poor data quality Sup.docx
1. What are the business costs or risks of poor data quality Sup.docx
 
Big data analytics for telecom operators final use cases 0712-2014_prof_m erdas
Big data analytics for telecom operators final use cases 0712-2014_prof_m erdasBig data analytics for telecom operators final use cases 0712-2014_prof_m erdas
Big data analytics for telecom operators final use cases 0712-2014_prof_m erdas
 
Big data analytics for telecom operators final use cases 0712-2014_prof_m erdas
Big data analytics for telecom operators final use cases 0712-2014_prof_m erdasBig data analytics for telecom operators final use cases 0712-2014_prof_m erdas
Big data analytics for telecom operators final use cases 0712-2014_prof_m erdas
 
Data Mining
Data MiningData Mining
Data Mining
 
data analytics lecture2.pptx
data analytics lecture2.pptxdata analytics lecture2.pptx
data analytics lecture2.pptx
 
Capturing Marketing Information to Fuel Growth
Capturing Marketing Information to Fuel GrowthCapturing Marketing Information to Fuel Growth
Capturing Marketing Information to Fuel Growth
 
Statistika dan Analisis Data
Statistika dan Analisis DataStatistika dan Analisis Data
Statistika dan Analisis Data
 
Data Mining
Data MiningData Mining
Data Mining
 
1. Introduction to Data Mining (12).pptx
1. Introduction to Data Mining (12).pptx1. Introduction to Data Mining (12).pptx
1. Introduction to Data Mining (12).pptx
 
Data mining-basic
Data mining-basicData mining-basic
Data mining-basic
 
Social Network Analysis
Social Network AnalysisSocial Network Analysis
Social Network Analysis
 
Data mining final year project in jalandhar
Data mining final year project in jalandharData mining final year project in jalandhar
Data mining final year project in jalandhar
 
Data mining final year project in ludhiana
Data mining final year project in ludhianaData mining final year project in ludhiana
Data mining final year project in ludhiana
 

Recently uploaded

“Deploying Large Language Models on a Raspberry Pi,” a Presentation from Usef...
“Deploying Large Language Models on a Raspberry Pi,” a Presentation from Usef...“Deploying Large Language Models on a Raspberry Pi,” a Presentation from Usef...
“Deploying Large Language Models on a Raspberry Pi,” a Presentation from Usef...
Edge AI and Vision Alliance
 
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
sunilverma7884
 
IPLOOK Remote-Sensing Satellite Solution
IPLOOK Remote-Sensing Satellite SolutionIPLOOK Remote-Sensing Satellite Solution
IPLOOK Remote-Sensing Satellite Solution
IPLOOK Networks
 
Types of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technologyTypes of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technology
ldtexsolbl
 
High Profile Girls call Service Pune 000XX00000 Provide Best And Top Girl Ser...
High Profile Girls call Service Pune 000XX00000 Provide Best And Top Girl Ser...High Profile Girls call Service Pune 000XX00000 Provide Best And Top Girl Ser...
High Profile Girls call Service Pune 000XX00000 Provide Best And Top Girl Ser...
bhumivarma35300
 
Salesforce AI & Einstein Copilot Workshop
Salesforce AI & Einstein Copilot WorkshopSalesforce AI & Einstein Copilot Workshop
Salesforce AI & Einstein Copilot Workshop
CEPTES Software Inc
 
Evolution of iPaaS - simplify IT workloads to provide a unified view of data...
Evolution of iPaaS - simplify IT workloads to provide a unified view of  data...Evolution of iPaaS - simplify IT workloads to provide a unified view of  data...
Evolution of iPaaS - simplify IT workloads to provide a unified view of data...
Torry Harris
 
RPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptx
RPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptxRPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptx
RPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptx
SynapseIndia
 
Best Practices for Effectively Running dbt in Airflow.pdf
Best Practices for Effectively Running dbt in Airflow.pdfBest Practices for Effectively Running dbt in Airflow.pdf
Best Practices for Effectively Running dbt in Airflow.pdf
Tatiana Al-Chueyr
 
Litestack talk at Brighton 2024 (Unleashing the power of SQLite for Ruby apps)
Litestack talk at Brighton 2024 (Unleashing the power of SQLite for Ruby apps)Litestack talk at Brighton 2024 (Unleashing the power of SQLite for Ruby apps)
Litestack talk at Brighton 2024 (Unleashing the power of SQLite for Ruby apps)
Muhammad Ali
 
July Patch Tuesday
July Patch TuesdayJuly Patch Tuesday
July Patch Tuesday
Ivanti
 
[Talk] Moving Beyond Spaghetti Infrastructure [AOTB] 2024-07-04.pdf
[Talk] Moving Beyond Spaghetti Infrastructure [AOTB] 2024-07-04.pdf[Talk] Moving Beyond Spaghetti Infrastructure [AOTB] 2024-07-04.pdf
[Talk] Moving Beyond Spaghetti Infrastructure [AOTB] 2024-07-04.pdf
Kief Morris
 
EuroPython 2024 - Streamlining Testing in a Large Python Codebase
EuroPython 2024 - Streamlining Testing in a Large Python CodebaseEuroPython 2024 - Streamlining Testing in a Large Python Codebase
EuroPython 2024 - Streamlining Testing in a Large Python Codebase
Jimmy Lai
 
Feature sql server terbaru performance.pptx
Feature sql server terbaru performance.pptxFeature sql server terbaru performance.pptx
Feature sql server terbaru performance.pptx
ssuser1915fe1
 
Introduction-to-the-IAM-Platform-Implementation-Plan.pptx
Introduction-to-the-IAM-Platform-Implementation-Plan.pptxIntroduction-to-the-IAM-Platform-Implementation-Plan.pptx
Introduction-to-the-IAM-Platform-Implementation-Plan.pptx
313mohammedarshad
 
The importance of Quality Assurance for ICT Standardization
The importance of Quality Assurance for ICT StandardizationThe importance of Quality Assurance for ICT Standardization
The importance of Quality Assurance for ICT Standardization
Axel Rennoch
 
How Social Media Hackers Help You to See Your Wife's Message.pdf
How Social Media Hackers Help You to See Your Wife's Message.pdfHow Social Media Hackers Help You to See Your Wife's Message.pdf
How Social Media Hackers Help You to See Your Wife's Message.pdf
HackersList
 
BT & Neo4j: Knowledge Graphs for Critical Enterprise Systems.pptx.pdf
BT & Neo4j: Knowledge Graphs for Critical Enterprise Systems.pptx.pdfBT & Neo4j: Knowledge Graphs for Critical Enterprise Systems.pptx.pdf
BT & Neo4j: Knowledge Graphs for Critical Enterprise Systems.pptx.pdf
Neo4j
 
Dublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptx
Dublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptxDublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptx
Dublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptx
Kunal Gupta
 
Vertex AI Agent Builder - GDG Alicante - Julio 2024
Vertex AI Agent Builder - GDG Alicante - Julio 2024Vertex AI Agent Builder - GDG Alicante - Julio 2024
Vertex AI Agent Builder - GDG Alicante - Julio 2024
Nicolás Lopéz
 

Recently uploaded (20)

“Deploying Large Language Models on a Raspberry Pi,” a Presentation from Usef...
“Deploying Large Language Models on a Raspberry Pi,” a Presentation from Usef...“Deploying Large Language Models on a Raspberry Pi,” a Presentation from Usef...
“Deploying Large Language Models on a Raspberry Pi,” a Presentation from Usef...
 
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
Girls call Kolkata 👀 XXXXXXXXXXX 👀 Rs.9.5 K Cash Payment With Room Delivery
 
IPLOOK Remote-Sensing Satellite Solution
IPLOOK Remote-Sensing Satellite SolutionIPLOOK Remote-Sensing Satellite Solution
IPLOOK Remote-Sensing Satellite Solution
 
Types of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technologyTypes of Weaving loom machine & it's technology
Types of Weaving loom machine & it's technology
 
High Profile Girls call Service Pune 000XX00000 Provide Best And Top Girl Ser...
High Profile Girls call Service Pune 000XX00000 Provide Best And Top Girl Ser...High Profile Girls call Service Pune 000XX00000 Provide Best And Top Girl Ser...
High Profile Girls call Service Pune 000XX00000 Provide Best And Top Girl Ser...
 
Salesforce AI & Einstein Copilot Workshop
Salesforce AI & Einstein Copilot WorkshopSalesforce AI & Einstein Copilot Workshop
Salesforce AI & Einstein Copilot Workshop
 
Evolution of iPaaS - simplify IT workloads to provide a unified view of data...
Evolution of iPaaS - simplify IT workloads to provide a unified view of  data...Evolution of iPaaS - simplify IT workloads to provide a unified view of  data...
Evolution of iPaaS - simplify IT workloads to provide a unified view of data...
 
RPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptx
RPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptxRPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptx
RPA In Healthcare Benefits, Use Case, Trend And Challenges 2024.pptx
 
Best Practices for Effectively Running dbt in Airflow.pdf
Best Practices for Effectively Running dbt in Airflow.pdfBest Practices for Effectively Running dbt in Airflow.pdf
Best Practices for Effectively Running dbt in Airflow.pdf
 
Litestack talk at Brighton 2024 (Unleashing the power of SQLite for Ruby apps)
Litestack talk at Brighton 2024 (Unleashing the power of SQLite for Ruby apps)Litestack talk at Brighton 2024 (Unleashing the power of SQLite for Ruby apps)
Litestack talk at Brighton 2024 (Unleashing the power of SQLite for Ruby apps)
 
July Patch Tuesday
July Patch TuesdayJuly Patch Tuesday
July Patch Tuesday
 
[Talk] Moving Beyond Spaghetti Infrastructure [AOTB] 2024-07-04.pdf
[Talk] Moving Beyond Spaghetti Infrastructure [AOTB] 2024-07-04.pdf[Talk] Moving Beyond Spaghetti Infrastructure [AOTB] 2024-07-04.pdf
[Talk] Moving Beyond Spaghetti Infrastructure [AOTB] 2024-07-04.pdf
 
EuroPython 2024 - Streamlining Testing in a Large Python Codebase
EuroPython 2024 - Streamlining Testing in a Large Python CodebaseEuroPython 2024 - Streamlining Testing in a Large Python Codebase
EuroPython 2024 - Streamlining Testing in a Large Python Codebase
 
Feature sql server terbaru performance.pptx
Feature sql server terbaru performance.pptxFeature sql server terbaru performance.pptx
Feature sql server terbaru performance.pptx
 
Introduction-to-the-IAM-Platform-Implementation-Plan.pptx
Introduction-to-the-IAM-Platform-Implementation-Plan.pptxIntroduction-to-the-IAM-Platform-Implementation-Plan.pptx
Introduction-to-the-IAM-Platform-Implementation-Plan.pptx
 
The importance of Quality Assurance for ICT Standardization
The importance of Quality Assurance for ICT StandardizationThe importance of Quality Assurance for ICT Standardization
The importance of Quality Assurance for ICT Standardization
 
How Social Media Hackers Help You to See Your Wife's Message.pdf
How Social Media Hackers Help You to See Your Wife's Message.pdfHow Social Media Hackers Help You to See Your Wife's Message.pdf
How Social Media Hackers Help You to See Your Wife's Message.pdf
 
BT & Neo4j: Knowledge Graphs for Critical Enterprise Systems.pptx.pdf
BT & Neo4j: Knowledge Graphs for Critical Enterprise Systems.pptx.pdfBT & Neo4j: Knowledge Graphs for Critical Enterprise Systems.pptx.pdf
BT & Neo4j: Knowledge Graphs for Critical Enterprise Systems.pptx.pdf
 
Dublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptx
Dublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptxDublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptx
Dublin_mulesoft_meetup_Mulesoft_Salesforce_Integration (1).pptx
 
Vertex AI Agent Builder - GDG Alicante - Julio 2024
Vertex AI Agent Builder - GDG Alicante - Julio 2024Vertex AI Agent Builder - GDG Alicante - Julio 2024
Vertex AI Agent Builder - GDG Alicante - Julio 2024
 

Data mining in Telecommunications

  • 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

Editor's Notes

  1. The knowledge was obtained by Human experts which was time consuming
  2. The actual data Mining task is automatic or semi-automatic analysis of large quantities of data
  3. Anomaly- Not to be proper sequence i.e. repetition of data
  4. Having these two components marketers can decide which marketing actions should we take for each segment To compete with the other providers of mobile telecommunications it is important to know enough about customer and to his know wants and needs
  5. Call-Detail Data describe the Calling Behaviour of each customer.
  6. Network Management functions such as FAULT ISOLATION
  7. Before the data can be used for the actual data mining process, it need to cleaned and prepared in a required format.
  8. In Marketing we analyse and profile Customer Behaviour and then accordingly, the profiles are used for marketing/forecasting purpose. Telecommunication companies maintain great deal of data about their Customers. MCI- Mobile Communication International NETWORK FAULT ISOLATION- Complex Configuration Contains many elements Elements may generate millions of status that lead to
  9. Data Mining softwares- Free source- RapidMiner, Carrot2 1.Oracle Data Mining 2. IBM SPSS Modeller 3. Microsoft Analysis services