Clustering Of Spatial Data Mining Methods And Applications

Data Mining has become an increasingly popular subject, which involves lots of scientific domains and technologies such as database, pattern recognition, neural network and computational intelligence. Spatial Data Mining is one research field of data mining, which can discover effective, novel, invaluable and understandable knowledge or rules from spatial database.Clustering is a very important technology and method of Data Mining as well as spatial clustering analysis is the main research field of Spatial Data Mining. With the help of spatial clustering analytical tools, not only clustering rules can be extracted in a large column of spatial database, but when combining with other data mining methods knowledge hidden deeply can be discovered efficiently and effectively as well. The contribution of this thesis has been concluded as follows.1. Summarizing the conceptions of Spatial Data Mining including theories, technology, methods, research content and development tendency. Besides, pointing out some unfathomed problems of Spatial Data Mining.2. Systemically analyzing and summarizing different spatial clustering algorithms that have been published in documents. The fitness, performance, advantages and disadvantages, and complexity of different algorithms have been compared in the paper.3. The Web-Based Data Mining Service System (MinerOnWeb) is a research achievement of data mining group in Computational Intelligence laboratory (CIlab), which can provide online data mining services. This dissertation described the functions, features, and system framework of MinerOnWeb and discussed the detail design in service site of EJB and web applications.4. Successfully designed and implemented the EJB service site as...