Spatial Mining and Temporal Mining

Spatial Mining

A spatial database saves a huge amount of space-related data, including maps, pre-processed remote sensing or medical imaging records, and VLSI chip design data. Spatial databases have several features that distinguish them from relational databases. They carry topological and/or distance information, usually organized by sophisticated, multidimensional spatial indexing structures that are accessed by spatial data access methods and often require spatial reasoning, geometric computation, and spatial knowledge representation techniques.

Spatial data mining refers to the extraction of knowledge, spatial relationships, or other interesting patterns not explicitly stored in spatial databases. Such mining demands the unification of data mining with spatial database technologies. It can be used for learning spatial records, discovering spatial relationships and relationships among spatial and nonspatial records, constructing spatial knowledge bases, reorganizing spatial databases, and optimizing spatial queries.

It is expected to have broad applications in geographic data systems, marketing, remote sensing, image database exploration, medical imaging, navigation, traffic control, environmental studies, and many other areas where spatial data are used.

A central challenge to spatial data mining is the exploration of efficient spatial data mining techniques because of the large amount of spatial data and the difficulty of spatial data types and spatial access methods. Statistical spatial data analysis has been a popular approach to analyzing spatial data and exploring geographic information.

The term geostatistics is often associated with continuous geographic space, whereas the term spatial statistics is often associated with discrete space. In a statistical model that manages non-spatial records, one generally considers statistical independence among different areas of data.

There is no such separation among spatially distributed records because, actually spatial objects are interrelated, or more exactly spatially co-located, in the sense that the closer the two objects are placed, the more likely they send the same properties. For example, natural resources, climate, temperature, and economic situations are likely to be similar in geographically closely located regions.

Such a property of close interdependency across nearby space leads to the notion of spatial autocorrelation. Based on this notion, spatial statistical modeling methods have been developed with success. Spatial data mining will create spatial statistical analysis methods and extend them for large amounts of spatial data, with more emphasis on effectiveness, scalability, cooperation with database and data warehouse systems, enhanced user interaction, and the discovery of new kinds of knowledge.

Temporal Mining

Temporal data refers to the extraction of implicit, non-trivial and potentially useful abstract information from large collection of temporal data. It is concerned with the analysis of temporal data and for finding temporal patterns and regularities in sets of temporal data tasks of temporal data mining are:

• Data Characterization and Comparison
• Cluster Analysis
• Classification
• Association rules
• Prediction and Trend Analysis
• Pattern Analysis