Data mining is the process of discovering useful patterns, relationships, and knowledge from large datasets by applying statistical, machine learning, and database techniques.
Data mining refers to the process of discovering useful patterns and insights from large datasets. It involves:
- Finding Useful Patterns: Identifying patterns in the data that can be used to make predictions or gain insights.
- Building Predictive Models: Using these patterns to create models that can predict future outcomes or behaviors.
The term “data mining” has evolved and is now often referred to as traditional machine learning, particularly focusing on supervised machine learning where models are built to make predictions based on labeled data.
Concise definition: Data mining = turning raw data into actionable knowledge by uncovering hidden patterns and trends.
Laws of Data Mining:
- There are always interactions & patterns
Key points:
Goal: extract meaningful information from raw data that is not obvious.
Methods used:
- Classification (assigning labels)
- Clustering (grouping similar items)
- Association rule mining (finding “if–then” patterns, e.g., market basket analysis)
- Anomaly detection (finding unusual patterns)
- Regression (predicting numerical values)