Spatial Data Mining
Definition: Spatial Data Mining is the process of discovering interesting patterns, relationships, or knowledge from spatial data, which includes data related to geographical or spatial locations. This type of mining extends traditional data mining techniques to handle spatial characteristics, such as coordinates, maps, and topology.
Key Features of Spatial Mining:
Spatial data includes points, lines, polygons, and raster data (e.g., images, satellite data).
Clustering: Grouping spatial data based on proximity or similarity (e.g., cities with similar weather patterns).
Classification: Categorizing spatial data into predefined classes (e.g., land use types).
Association Rules: Discovering relationships (e.g., "forests are often near rivers").
Trend Analysis: Identifying patterns across spatial areas (e.g., urban expansion).
Geographical Information Systems (GIS): Analyzing and managing spatial data for urban planning, agriculture, and resource management.
Environmental Studies: Predicting climate patterns or tracking deforestation.
Navigation Systems: Improving map-based recommendations or routing.
Temporal Data Mining
Definition: Temporal Data Mining focuses on extracting meaningful patterns and trends from time-series data or datasets where time is a critical factor. It involves analyzing changes, sequences, and temporal dependencies over time.
Key Features of Temporal Mining: