Data Mining

Data Mining is the process of discovering useful patterns, relationships, and knowledge from large volumes of data. It helps organizations make data-driven decisions.

1. What is Data Mining?

Data Mining extracts hidden and meaningful information from large datasets using statistical and machine learning techniques.

2. Data Mining vs Data Warehouse

Data Warehouse                Data Mining
---------------------------  -----------------------------
Stores historical data        Extracts knowledge
Used for reporting            Used for prediction
Data repository               Analysis process

3. Data Mining Process

Data Collection
      ↓
Data Cleaning
      ↓
Data Integration
      ↓
Data Selection
      ↓
Data Mining
      ↓
Pattern Evaluation
      ↓
Knowledge Presentation

4. Types of Data Mining Tasks

5. Classification

Classification assigns data items to predefined categories.

Example:
Student → Pass / Fail
Email   → Spam / Not Spam

6. Clustering

Clustering groups similar data items without predefined labels.

Example:
Customers grouped by buying behavior

7. Association Rule Mining

Discovers relationships between items in large datasets.

Example:
If a customer buys Bread → likely buys Butter

8. Prediction

Prediction estimates future outcomes based on historical data.

Example:
Predict sales for next month
Predict student performance

9. Applications of Data Mining

10. Advantages of Data Mining

11. Limitations of Data Mining

Practice Questions

  1. What is data mining?
  2. Differentiate data mining and data warehouse.
  3. Explain data mining process.
  4. What are types of data mining tasks?
  5. List applications of data mining.

Practice Task

Explain with examples: ✔ Classification ✔ Clustering ✔ Association rule mining ✔ Prediction