UNIT-1
DATA MINING
What is Data Mining?
Data mining means extracting useful knowledge from large amounts of raw data.
➔ Not just storing data
➔ Not just viewing data
➔ We analyze and discover hidden patterns
Y
Definition
Data mining is the process of discovering hidden patterns, correlations, trends,
and useful knowledge from large datasets using statistical, machine learning, and
database techniques.
Key Words in Definition
Hidden
Word
Patterns
Trends
Large datasets
hitr Meaning
Not obvious at first look
Repeated relationships
Changes over time
Big data (lakhs/millions of
records)
a
Intelligent AI, ML, statistics
techniques
A
Why Data Mining Exists
Earlier:
➔ Data stored only for records
➔ No deep analysis
Now:
➔ Huge digital data
➔ Need insights
,Example:
Banks store crores of transactions
But real value = detecting fraud
3. Where Data Mining is Used
1. Banking
➔ Fraud detection
➔ Credit scoring
➔ Loan approval prediction
Example:
If unusual spending → block card
Y
2. Healthcare
➔ Disease prediction
➔ Patient risk analysis
➔ Drug discovery
Example:
Early diabetes detection using patient data
3. Education hi
➔ Predict student performance
t
➔ Identify weak students
➔ Improve teaching
Example:
r
Students with <60% attendance → high failure risk
a
4. E-Commerce
➔ Product recommendations
A
➔ Customer behavior analysis
➔ Sales prediction
Example:
“Customers who bought laptop also bought mouse”
5. Social Media
➔ Friend suggestions
➔ Content recommendation
➔ Trend analysis
, Example:
Instagram reels based on watch history
What Exactly Does Data Mining Do?
Data mining converts:
Raw Data → Information → Knowledge → Decisions
Example
Restaurant daily data:
➔ Orders
➔ Time
Y
➔ Day
➔ Items
After mining:
➔ Pizza sells more on weekends
➔ Biryani sells more at night
Helps:
★ Stock planning
hi
★ Profit increase
Types of Knowledge Discovered
t
Data mining finds:
1. Patterns-Repeated behavior
r
Example:Students who study daily score high
a
2. Associations-Items that occur together
Example:Tea → Biscuits
A
3. Trends-Changes over time
Example:Ice cream sales increase in summer
4. Predictions-Future values
Example:Predict next month sales
5. Classification Rules-Grouping data
Example:Spam vs Not spam email
DATA MINING
What is Data Mining?
Data mining means extracting useful knowledge from large amounts of raw data.
➔ Not just storing data
➔ Not just viewing data
➔ We analyze and discover hidden patterns
Y
Definition
Data mining is the process of discovering hidden patterns, correlations, trends,
and useful knowledge from large datasets using statistical, machine learning, and
database techniques.
Key Words in Definition
Hidden
Word
Patterns
Trends
Large datasets
hitr Meaning
Not obvious at first look
Repeated relationships
Changes over time
Big data (lakhs/millions of
records)
a
Intelligent AI, ML, statistics
techniques
A
Why Data Mining Exists
Earlier:
➔ Data stored only for records
➔ No deep analysis
Now:
➔ Huge digital data
➔ Need insights
,Example:
Banks store crores of transactions
But real value = detecting fraud
3. Where Data Mining is Used
1. Banking
➔ Fraud detection
➔ Credit scoring
➔ Loan approval prediction
Example:
If unusual spending → block card
Y
2. Healthcare
➔ Disease prediction
➔ Patient risk analysis
➔ Drug discovery
Example:
Early diabetes detection using patient data
3. Education hi
➔ Predict student performance
t
➔ Identify weak students
➔ Improve teaching
Example:
r
Students with <60% attendance → high failure risk
a
4. E-Commerce
➔ Product recommendations
A
➔ Customer behavior analysis
➔ Sales prediction
Example:
“Customers who bought laptop also bought mouse”
5. Social Media
➔ Friend suggestions
➔ Content recommendation
➔ Trend analysis
, Example:
Instagram reels based on watch history
What Exactly Does Data Mining Do?
Data mining converts:
Raw Data → Information → Knowledge → Decisions
Example
Restaurant daily data:
➔ Orders
➔ Time
Y
➔ Day
➔ Items
After mining:
➔ Pizza sells more on weekends
➔ Biryani sells more at night
Helps:
★ Stock planning
hi
★ Profit increase
Types of Knowledge Discovered
t
Data mining finds:
1. Patterns-Repeated behavior
r
Example:Students who study daily score high
a
2. Associations-Items that occur together
Example:Tea → Biscuits
A
3. Trends-Changes over time
Example:Ice cream sales increase in summer
4. Predictions-Future values
Example:Predict next month sales
5. Classification Rules-Grouping data
Example:Spam vs Not spam email
