Costarch Analytical
Consulting Pvt Ltd.
Data Mining
 Why data mining

 What is data mining
 Scope of data mining
 Typical applications
 Issues with data mini...
What it is?
Data Mining Contd…..
 A data mining research carried out at supermarkets showed that,
 Men who had children and who do s...
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 w...
Data Mining Contd..
 IN CRM , it is a well known fact that acquiring a new customer cost 7 times

more than keeping the e...
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 ...
What is Data Mining
It is a process of discovering
 suitable
 New
 Potentially useful insights
 Under stable patterns ...
Stages of Data mining
Selection
•Segmenting the data according to criteria
•Example who have a car, people who are in govt...
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

E...
Scope of Data Mining
 Data Mining technology can generate new business opportunities
 Automated prediction of trends and...
Data Mining Limitations
 Data mining systems relies on databases to supply the raw data for output.
 Problem occurs as d...
Techniques used in Data Mining
 Artificial Neural Networks
 Decisions Trees
 Genetic Algorithm
 Nearest Neighbor Metho...
Artificial Neural Networks

o System of interconnected neurons

that can compute values from
inputs by feeding information...
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



B...
Contd..
 Media logistic


Academic Research



IT & ITES



Online Portals and Social Media Channels



Media and Adv...
Contact Us
If you have any questions , please let us know at
info@costarch.com or Call us +91-8955678210
OR
Visit our webs...
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Data mining and its applications!

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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.

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

    1. 1. Costarch Analytical Consulting Pvt Ltd.
    2. 2. Data Mining  Why data mining  What is data mining  Scope of data mining  Typical applications  Issues with data mining
    3. 3. What it is?
    4. 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. 5. Data Mining Contd..  helps in churn prevention.
    6. 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. 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. 8. Why Data Mining is important ? Data
    9. 9. Why Data Mining is important?
    10. 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. 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. 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. 13. Reasons for Data Mining popularity  Growing Data Volume  Limitations of Human Analysis  Low Cost of Machine Learning
    14. 14. Data Mining Models  Verification Model  Discovery Model
    15. 15. Data Mining Process Model Understanding business requirements Deployment Data collection Data Data prep and analysis Evaluation Data modeling
    16. 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. 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. 18. Techniques used in Data Mining  Artificial Neural Networks  Decisions Trees  Genetic Algorithm  Nearest Neighbor Method  Rule Induction
    19. 19. Artificial Neural Networks o System of interconnected neurons that can compute values from inputs by feeding information through the network.
    20. 20. Decision Tree  Uses tree like model of decisions and their possible consequences
    21. 21. Genetic Algorithm o Generate useful solutions to optimization and search problems.
    22. 22. Nearest Neighbor Method  Classifying cases based on their similarity to other cases
    23. 23. Rule induction  The extraction of useful if-then rules from data based on statistical significance.
    24. 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. 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. 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

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