- Introduction and Process
- Data mining finds patterns and correlations in large datasets for forecasting results
- Process includes data acquisition, cleaning, feature selection, and algorithm choice
- Example: Superstore owner discovered milk-bread purchase patterns
- WEKA Features
- WEKA is an open-source toolkit for data mining tasks
- Supports Java 8+ and runs on Windows, MAC OS, and Linux
- Contains Explorer, Experimenter, KnowledgeFlow, and CLI panels
- Supports ARFF, CSV, JSON, and XRFF data formats
- Data Types and Processing
- Handles numeric, string, date, and relational data types
- Provides comprehensive data preprocessing options
- Supports loading data from local files, URLs, databases, and artificial data
- Main Features
- Offers classification, clustering, and association rule mining algorithms
- Includes attribute selection tools and visualization capabilities
- Provides various test options for classifier evaluation
- Supports multiple classification algorithms including ZeroR and OneR