Spatial Data Mining & Types of Web Mining

What is Spatial Data Mining?

Spatial data mining is the process of discovering interesting, useful, and previously unknown patterns from large spatial (geographic) databases — data in which objects are described by their location in space (e.g., maps, satellite imagery, GPS traces).

The distinguishing feature is that ordinary mining techniques must be extended to account for spatial relationships such as distance, adjacency, overlap, and containment between neighbouring objects. Common tasks include:

• Spatial clustering — grouping geographically close objects (e.g., identifying high-crime zones).
• Spatial outlier detection — finding locations whose attribute values differ sharply from their neighbourhood (e.g., an unusually high-pollution monitor).
• Spatial association rules — e.g., "regions that are close to a city centre and near a transport hub tend to have high land value".
• Spatial trend / classification — predicting categorical values from spatial features.

Examples of spatial applications:

• Satellite remote-sensing (land-use/land-cover classification, weather & vegetation monitoring).
• GIS (Geographic Information Systems) for urban planning and route optimisation.
• Location-based services (traffic prediction, targeted advertising) using mobile GPS data.
• Environmental monitoring (earthquake prediction, pollution mapping, disease-spread tracking).

Web Mining and its Major Types

Web mining is the application of data-mining techniques to discover useful patterns from web data — the content of web pages, the hyperlink structure of the web, and usage records of how users interact with websites. Its three major types are:

1. Web Content Mining — discovers useful information from the content of web pages (text, images, structured data). It includes retrieval (search), classification and clustering of pages, and extraction of structured information (e.g., product metadata). Techniques involve NLP, text mining, and wrapper induction.

2. Web Structure Mining — analyses the hyperlink topology — that is the graph of pages and links — to infer authority, ranking, and communities. Examples: Google's PageRank, HITS (hyperlink-induced topic search). Two aspects: intra-document structure and inter-document hyperlinks.

3. Web Usage Mining — mines the log records generated by servers, browsers, and proxies when users browse a website — that is, how users access the site. Its goal is usage patterns (frequent navigation paths, sessions) for applications such as personalisation, pre-fetching, improving site design, and e-commerce recommendation.

Summary table:

Type	Data source	Goal
Content mining	Page content (text, media)	Extract/classify useful information
Structure mining	Hyperlink graph	Discover authority/hub pages, communities
Usage mining	Server/browser logs, cookies	Understand & predict user behaviour