data mining, data preprocessing, data cleaning, knowledge discovery, association, classification, clustering, introduction, why data mining, application

1. Data Mining Fundamentals
Presented by
S.P. Siddique Ibrahim AP/CSE
Kumaraguru College of
Technology
09/06/18
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3. DATA
• Fact, Numbers (or) text.
• Different format and different databases.
• Examples:
• College department,CoE,Hostal, Payroll,
• Police traffic, fraud data
• RTO
• Revenue
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5. DATA Contd.,
• Operational or Transactional – sales, cost,
inventory, payroll
• Non operational- industry sales , forecasting
data, micro economic data
• Meta data – logical database design (or)
dictionary design
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6. Information
• The pattern, relationships among all this data
can provide information.
• Example:
• Analysis retail point of sale transaction
• Product –selling and date
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7. Knowledge
• Information can be converted into knowledge
about historical patterns and future trends.
• Example:
• Summary information of petrol/Diesel usage.
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media/siddique/Files1/Kct/Siddique/Datami
ning Lab/id93_480x480.jpg

11. General Definition of Mining
• Mining is the process of extracting commercially
valuable minerals resources from earth’s surface.
• -Stones
• -Ores
• -Solid Fuels
• -Crude Oil
 Surface Mining
 Subsurface mining
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14. Definition contd.,
• Analyzing data from different perspective and
summarize information.
• Information-revenue
• Technically, it is the process of finding
correlation (or) patterns among dozens of fields
in large relational databases.
• Example: Purchase pattern, credit card
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15. Data Mining
• Data mining, “the extraction of hidden predictive
information from large databases”.
• It is a powerful new technology with great potential to
help companies focus on the most important information
in their data warehouses.
• The main focus of data mining process is to obtain
information from the data and converted it into an
knowledgeable and reasonable structure for further use.
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16. Different Mining in Real Time
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18. Data set
bank-data.csv
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19. What is Machine Learning??
Machine learning is a field of computer science
that uses statistical techniques to give computer
systems the ability to "learn" with data, without
being explicitly programmed.
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20. Why data mining for you?
• Research Opportunities.
• High Opening.
• Handling Big data.
• Analysis capability
• Future
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21. What can data mining do?
• Company competition
• Internal and external factor
• Analyze real feelings of customer/Auditions
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24. What is pattern? How it would be?
• Customer buying pattern
• Mobile pattern
• Bus ticket booking
• Cricket analysis
• Political parties election freebies
• Credit card
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25. Amazon recommendation
Big Bazar
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29. Password Hacking
ATM Machine-Fraud
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32. What is Data Warehouse????
A data warehousing is a technique for collecting
and managing data from varied sources to
provide meaningful business insights.
Online Airline/Bus/Railway ticket booking
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33. Data warehouse
• Centralized storage
• Free use of data
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34. How does data mining work?
• Classes – stored data is used to locate data in
predetermined groups.
• Example:
• Restaurant chain
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36. What is this? Classify it!!!!
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38. Can u able to Identify Now!!??
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39. Cluster
• Data items are grouped based on
similarity(logical relationship) (or) consumer
preferences.
•
• Example:
• Data used for market segments
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40. Association
• Mined for associations.
Example:
Market basket analysis
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41. Sequential patterns
• Data is mined to anticipate behavior patterns
and trends.
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42. Regression Analysis
• A measure of the relation between the mean
value of one variable(E.g. Output) and
corresponding values of other variables(E.g
Time and cost)
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59. https://create.kahoot.it/kahoots/my-kahoots
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