Definition
Data Mining
The process of discovering patterns, correlations, and insights from large datasets using statistical and computational techniques.
Key Approaches to Data Mining
- Cluster Analysis
- Cluster analysis groups similar data points into clusters based on shared characteristics.
- How it works: Algorithms like k-means or hierarchical clustering assign data points to clusters by minimizing the distance between points within the same cluster.
- Applications: Market segmentation, image recognition, and anomaly detection.
- A retail company might use cluster analysis to segment customers into groups based on purchasing behavior, such as frequent buyers, occasional shoppers, and one-time customers.
- Associations
- Association rule mining identifies relationships between variables in large datasets.
- How it works: Algorithms generate rules in the form of "If X, then Y," where X and Y are items or events.
- Applications: Market basket analysis, recommendation systems, and cross-selling strategies.
- A supermarket might discover that customers who buy bread often buy butter, leading to targeted promotions.
- Classifications
- Classification assigns data points to predefined categories or classes.
- How it works: Algorithms like decision trees or neural networks learn from labeled data to predict the class of new, unseen data.
- Applications: Fraud detection, medical diagnosis, and sentiment analysis.
- An email service might use classification to filter spam emails from legitimate messages.
- Sequential Patterns
- Sequential pattern mining identifies recurring sequences of events or actions.
- How it works: Algorithms analyze time-ordered data to find frequent sequences.
- Applications: Customer behavior analysis, web usage mining, and process optimization.
- An online retailer might discover that customers who buy a laptop often purchase a mouse and then a laptop bag in subsequent transactions.
