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Data Mining
MIS 125 Presentation
Serkan Uzundönekoğlu
Yavuz Selim Kamacıhan
Table of Content
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What is data mining?
Similiarities between gold mining and data mining.
Alternative names of data mining.
Examples of the areas data mining is used.
Diciplines that are used for data mining.
Main data mining tasks.
Why is data mining important?
Your information is valuable.
Data mining styles.
Why data mining is necessary?
Video
What is Data Mining?
• Extraction of interesting
information or patterns from data
in large databases
• Data mining is also known as
knowledge discovery
• EG: Gold mining can be indicated
as a similiar process to knowledge
discovery
Gold Mining
Data Mining
Other Alternative Names
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Knowledge Extraction
Data/pattern analysis
Data archeology
Information Harvesting
YOUR DATA >> FOR SALE !
• Facebook is being sued for selling private information of its users for
particular advertising purposes.
Hospital Records are also being mined to sell
pharmaceutical companies.
• AND IT IS
ALL LEGAL!
Data Mining: Confluence of Multiple
Disciplines
Main Data Mining Tasks
• Classification
• mining patterns that can classfiy future data into known classes.
• Association rule mining
• mining any rule of the form X  Y, where X and Y are sets
of data items.
• Clustering
• identifying a set of similarity groups in the data.
Why is Data Mining Important?
• Rapid computerization of businesses produce huge amount of data
• How to make best use of data?
• A growing realization: knowledge discovered from data can be used for
competitive advantage.
Your Information is Valuable!
Companies want to know your:
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Age
Gender
Income level
Style of clothing
Where you live
Number of children you have
What kind of car you drive
How much time you spend online and
Where you spend it
Data Mining is Valuable !
With the help of those information provided by data mining helps
companies to determine relationship between consumer behaviour
and sales.
It allows to companies gain competetive
advantage and increase profitability
Data Mining Styles
• Descriptive
• characterize the general properties of the data in the database
• Predictive
• perform inference on the current data to make predictions
• Not Mutually Exclusive
• Descriptive  predictive
 Customer segmentation – descriptive by clustering
 Followed by a risk assignment model – predictive
Why is data mining necessary?
• There is a big gap from stored data to knowledge; and the transition won’t
occur automatically.
• Many interesting things you want to find cannot be found using database
queries
“find me people likely to buy my products”
“Who are likely to respond to my promotion”
Video
http://www.youtube.com/watch?v=R-sGvh6tI04