# Introduction to Data Mining

## What is Data Mining?
**Data Mining** is the process of digging through large amounts of raw data to find useful patterns, trends, and knowledge.
- **Analogy**: Like mining gold from rocks. The rocks are the "raw data," and the gold is the "knowledge."

### Key Definitions
- **Data**: Raw facts and figures (e.g., sales logs, sensor readings).
- **Mining**: Extracting something valuable.

## The DIKW Pyramid
The **DIKW** model shows how we move from raw data to wisdom.

1. **Data (D)**: Raw, unprocessed facts.
   - *Example*: Numbers like 42, 35, 50.
2. **Information (I)**: Data that is organized and has meaning.
   - *Example*: "These are the ages of employees."
3. **Knowledge (K)**: Understanding gained from analysis.
   - *Example*: "The team has a mix of young and experienced people."
4. **Wisdom (W)**: Applying knowledge to make good decisions.
   - *Example*: "Let's create a mentorship program to share skills."

## Major Issues in Data Mining
1. **Privacy and Security**: Mining can reveal sensitive personal info. We must protect it.
2. **Scalability**: Can the system handle huge amounts of data (Big Data)?
3. **Data Quality**: If data is dirty or missing, the results will be wrong ("Garbage In, Garbage Out").
4. **Ethical Use**: Ensuring data isn't used for discrimination or bias.