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Data Mining

By Mr. L. Abraham David   |   St.John’s College, Palayamkottai Tirunelveli
Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Data mining is the extraction of hidden predictive information from large databases is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. 

Data mining methodology extracts hidden predictive information from large databases. With such a broad definition, however, an online analytical processing (OLAP) product or a statistical package could qualify as a data mining tool. Retail companies and the financial community are using data mining to analyze data and recognize trends to increase their customer base, predict fluctuations in interest rates, stock prices, and customer demand.


Learners enrolled: 7178

SUMMARY

Course Status : Completed
Course Type : Core
Duration : 12 weeks
Start Date : 06 Aug 2019
End Date : 10 Oct 2019
Exam Date : 10 Nov 2019
Enrollment Ends : 10 Sep 2019
Category :
  • Computer Science and Engineering
  • Level : Undergraduate

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    COURSE LAYOUT


    Week – I 

    1. Introduction to Data Mining
    2. Introduction to Data Warehousing
    3. OLTP
    4. Trends in Data Warehousing

    Week – II  

    5. Applications in Data Warehousing
    6. Data Warehousing Architecture-I
    7. Data Warehousing Architecture-II
    8. Data Warehousing Architecture-III

    Week – III

    9. Data Warehousing Architecture-IV
    10.Data Warehousing Architecture-V 
    11. Data Mining-I
    12. Data Mining-II
         
    Week – IV

    13. Data Mining-III
    14. Data Mining-IV
    15. Data Mining-V
    16. Data Mining-VI

    Week – V

    17. Data Mining-VII
    18. Association Rule Mining-I
    19. Association Rule Mining-II

    Week – VI  

    20. Association Rule Mining-III
    21. Association Rule Mining-IV
    22. Association Rule Mining-V

    Week – VII  

    23. Classification Techniques-I
    24. Classification Techniques-II
    25. Classification Techniques-III
    26. Classification Techniques-IV

    Week – VIII

    27. Classification Techniques-V
    28. Classification Techniques-VI
    29. Classification Techniques-VII

    Week – IX
    30. Other Data Mining Methods-I
    31. Other Data Mining Methods-II
    32. Other Data Mining Methods-III
    33. Other Data Mining Methods-IV

    Week – X

    Interaction




    BOOKS AND REFERENCES

    1. Jiawel Han and Micheline Kamber, “Data mining concepts and Techniques”
    2. Berson, “Data Warehousing,Data Mining, OLAP”
    3. Pang-Ning Tan,  Michael Steinbach, Vipin Kumar “ Introduction to Data Mining”
    4. Arun K Pujari, “Data Mining Techniques”
    5. Dunham M H, “Data Mining-Introductory and Advanced Topics”

    INSTRUCTOR BIO



    Mr. L. Abraham David is an Assistant Professor in St.John’s College, Palayamkottai Tirunelveli. He has Five years of Teaching experience. He has attended many confernceses seminars and Workshops.

    COURSE CERTIFICATE

    30% for in course Assessment & 70% of end term Proctored Exam


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