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Data Mining Techniques and Applications

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COSTARCH Analytical Consulting (P) Ltd.
COSTARCH Analytical Consulting (P) Ltd.

This Presentation is about Data mining and its application in different fields. This presentation shows why data mining is important and how it can impact businesses.

Technology
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Costarch Analytical Consulting Pvt Ltd.
Data Mining  Why data mining  What is data mining  Scope of data mining  Typical applications  Issues with data mining
What it is?
Data Mining Contd…..  A data mining research carried out at supermarkets showed that,  Men who had children and who do shopping on Saturday to buy nappies for their little ones tend to buy Beer also….  Interesting right?.....  Lets see some more examples……
Data Mining Contd..  helps in churn prevention.
Data mining Contd.. o Data mining in CRM o Based on the behavior and personal data about the customer, CRM admisnitrter will divide the customer in two classes. o A Prediction model can be created using this data to find the probability of losing a customer in next two years.
Data Mining Contd..  IN CRM , it is a well known fact that acquiring a new customer cost 7 times more than keeping the existing customer (churn prevention)  Thus CRM can help to reduce the churn rate
Why Data Mining is important ? Data
Why Data Mining is important?
Why Data Mining  Terabytes of data generated every day  Abundance of data available from sources like business, society and science  Easy techniques of data collection ex: automated data collection tools, database systems, computerized society etc  Need for analysis of massive data
What is Data Mining It is a process of discovering  suitable  New  Potentially useful insights  Under stable patterns and trends in the large data sets  Using sophisticated mathematical algorithms  To segment the data  Evaluate the probability for future events
Stages of Data mining Selection •Segmenting the data according to criteria •Example who have a car, people who are in govt jobs Preprocessing •Data cleansing stage, unnecessary information is removed Transformation . Data is transformed. Data is made usable and navigable Data mining . Stage of extracting of patterns from data Interpretation and evaluation •Patterns interpreted into knowledge that can be used to support human decision making,
Reasons for Data Mining popularity  Growing Data Volume  Limitations of Human Analysis  Low Cost of Machine Learning
Data Mining Models  Verification Model  Discovery Model
Data Mining Process Model Understanding business requirements Deployment Data collection Data Data prep and analysis Evaluation Data modeling
Scope of Data Mining  Data Mining technology can generate new business opportunities  Automated prediction of trends and behaviors  Automated discovery of previously unknown patterns  Yield the benefits of automation on existing software and hardware platforms  Implemented on new systems as existing platforms are upgraded and new products developed
Data Mining Limitations  Data mining systems relies on databases to supply the raw data for output.  Problem occurs as data bases tend to be dynamic, incomplete, noisy and large  Uncertainty  Size and updating problem  Limited information
Techniques used in Data Mining  Artificial Neural Networks  Decisions Trees  Genetic Algorithm  Nearest Neighbor Method  Rule Induction
Artificial Neural Networks o System of interconnected neurons that can compute values from inputs by feeding information through the network.
Decision Tree  Uses tree like model of decisions and their possible consequences
Genetic Algorithm o Generate useful solutions to optimization and search problems.
Nearest Neighbor Method  Classifying cases based on their similarity to other cases
Rule induction  The extraction of useful if-then rules from data based on statistical significance.
Data Mining Application Data mining techniques can be applied in various fields such as  Telecommunication  CRM  Banking  Medicine and Pharmaceuticals  Insurance  Management (Quality Assurance, Marketing)  Travel and Tourism..
Contd..  Media logistic  Academic Research  IT & ITES  Online Portals and Social Media Channels  Media and Advertising  Airline Companies  Sports  Finance  Film Industry and many more etc.
Contact Us If you have any questions , please let us know at info@costarch.com or Call us +91-8955678210 OR Visit our website: www.costarch.com www.pietutors.com

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Data Mining Techniques and Applications

  • 2. Data Mining  Why data mining  What is data mining  Scope of data mining  Typical applications  Issues with data mining
  • 4. Data Mining Contd…..  A data mining research carried out at supermarkets showed that,  Men who had children and who do shopping on Saturday to buy nappies for their little ones tend to buy Beer also….  Interesting right?.....  Lets see some more examples……
  • 5. Data Mining Contd..  helps in churn prevention.
  • 6. Data mining Contd.. o Data mining in CRM o Based on the behavior and personal data about the customer, CRM admisnitrter will divide the customer in two classes. o A Prediction model can be created using this data to find the probability of losing a customer in next two years.
  • 7. Data Mining Contd..  IN CRM , it is a well known fact that acquiring a new customer cost 7 times more than keeping the existing customer (churn prevention)  Thus CRM can help to reduce the churn rate
  • 8. Why Data Mining is important ? Data
  • 9. Why Data Mining is important?
  • 10. Why Data Mining  Terabytes of data generated every day  Abundance of data available from sources like business, society and science  Easy techniques of data collection ex: automated data collection tools, database systems, computerized society etc  Need for analysis of massive data
  • 11. What is Data Mining It is a process of discovering  suitable  New  Potentially useful insights  Under stable patterns and trends in the large data sets  Using sophisticated mathematical algorithms  To segment the data  Evaluate the probability for future events
  • 12. Stages of Data mining Selection •Segmenting the data according to criteria •Example who have a car, people who are in govt jobs Preprocessing •Data cleansing stage, unnecessary information is removed Transformation . Data is transformed. Data is made usable and navigable Data mining . Stage of extracting of patterns from data Interpretation and evaluation •Patterns interpreted into knowledge that can be used to support human decision making,
  • 13. Reasons for Data Mining popularity  Growing Data Volume  Limitations of Human Analysis  Low Cost of Machine Learning
  • 14. Data Mining Models  Verification Model  Discovery Model
  • 15. Data Mining Process Model Understanding business requirements Deployment Data collection Data Data prep and analysis Evaluation Data modeling
  • 16. Scope of Data Mining  Data Mining technology can generate new business opportunities  Automated prediction of trends and behaviors  Automated discovery of previously unknown patterns  Yield the benefits of automation on existing software and hardware platforms  Implemented on new systems as existing platforms are upgraded and new products developed
  • 17. Data Mining Limitations  Data mining systems relies on databases to supply the raw data for output.  Problem occurs as data bases tend to be dynamic, incomplete, noisy and large  Uncertainty  Size and updating problem  Limited information
  • 18. Techniques used in Data Mining  Artificial Neural Networks  Decisions Trees  Genetic Algorithm  Nearest Neighbor Method  Rule Induction
  • 19. Artificial Neural Networks o System of interconnected neurons that can compute values from inputs by feeding information through the network.
  • 20. Decision Tree  Uses tree like model of decisions and their possible consequences
  • 21. Genetic Algorithm o Generate useful solutions to optimization and search problems.
  • 22. Nearest Neighbor Method  Classifying cases based on their similarity to other cases
  • 23. Rule induction  The extraction of useful if-then rules from data based on statistical significance.
  • 24. Data Mining Application Data mining techniques can be applied in various fields such as  Telecommunication  CRM  Banking  Medicine and Pharmaceuticals  Insurance  Management (Quality Assurance, Marketing)  Travel and Tourism..
  • 25. Contd..  Media logistic  Academic Research  IT & ITES  Online Portals and Social Media Channels  Media and Advertising  Airline Companies  Sports  Finance  Film Industry and many more etc.
  • 26. Contact Us If you have any questions , please let us know at info@costarch.com or Call us +91-8955678210 OR Visit our website: www.costarch.com www.pietutors.com

Editor's Notes

  1. Everything is sold and advertisedoninternext
  2. Models inspired by animal CNS , that are capable of machine learning and pattern reorganization.
  3. Decision support toolIncluding chance event outcomes, resource cost, utilityCommonly used in operation research, specifically decision analysis
  4. mimics the process of natural evolutionbioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, physics etc..