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A Research On Spatial Data Mining

Posted on:2004-11-19Degree:DoctorType:Dissertation
Country:ChinaCandidate:H Y ZhouFull Text:PDF
GTID:1118360095955973Subject:Cartography and Geographic Information Engineering
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Spatial data mining (SDM) refers to picking up interesting rules from spatial database, such as spatial patterns and characteristics, the universal relations of spatial and non-spatial data and other universal implicated in spatial data. This thesis studies on the theories, techniques and the applications of SDM. The main content of this paper include the follows:(1) The basic theory and technology framework of spatial data mining are established and the theory and methods are perfectly developed. The definition and characteristics of SDM are set forth, and a structure of spatial data mining system including data source, miner and user interface is put forward. The essential processes of SDM are studied and nine types of rules resulting in mining are discussed. There are 17 kinds of spatial data mining approaches researched in this paper and each method's characteristics are analyzed. Moreover, the difference and relationship between SDM and other related subjects are discussed in detail. The future research directions of SDM and some principles on developing SDM system are pointed out.(2) The theory of Rough Sets is introduced into SDM domain. The basal theory and techniques of Rough Sets including the basic notion and character, the extended models, the classification of knowledge expression system in spatial database, the coherence analysis of attribute table, the relying relations between attributes, the importance of attribute, the reduction of attribute and the reduction of attribute value in decision table are studied by the numbers.(3) The definition of spatial association rule is defined as the spatial and non-spatial relations between spatial objects. The forms of spatial association rule are abundant. Two important types of spatial association rule are studied. Firstly, The notion of the form as A=>B[s%, c%] is researched and some algorithms are discussed. An algorithm named A Progressive Refinement Approach to Spatial Data Mining is discussed in detail. And a new thought of mining spatial association rule based on spatial data cube is brought forward. Then, the spatial statistical analysis techniques are introduced into SDM domain. The measurement functions of spatial weight matrix, spatial auto-correlation and spatial association are studied.(4) Seven kinds of spatial data clustering approaches are studied. And the technique to solve the problem of Constraint-based Spatial Cluster Analysis is explored. In addition, a new spatial clustering algorithm based on Genetic Algorithms is set forward and it can give attention to local constringency and the whole constringency.(5) The definitions, characteristics and all kinds of building algorithms of the Voronoi Diagram and the Delaunay Triangle are introduced. Their applications in SDM are explored. That the Voronoi Diagram is an effective method to partition the influence regions between spatial objects and phenomena is put forward, and that the principle of building Voronoi Diagram is identical to the forming central place is proved. Then, that the Delaunay Triangle is the best model to set up the cities network is brought forward. Finally, the problem of spatial establishment location selection by means of the Voronoi Diagram is studied.
Keywords/Search Tags:Data Mining, Spatial Data Mining, Spatial Data Warehouse, Rough Sets, Spatial Association Rules, Spatial Statistical Analysis, Spatial Data Clustering, Genetic Algorithms, Voronoi Diagram, Delaunay Triangle.
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