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SPAM: An Effective and Efficient Spatial Algorithm for Mining Grid Data
Abstract
The tremendous increase in spatial database technology has created furious interest among the researchers to adopt new methodologies for discovery of interesting patterns among large databases. But the raw data gathered from various resources such as Geographic Information Systems (GIS), business organizations, medical databases, climatic, market survey, remote sensing and several other resources might consist of data, which can be relevant, irrelevant or noisy in nature. However, retrieval of patterns from such databases can lead to serious concerns, which can frame inconsistent or irrelevant futuristic results. To deal with such issues, feature selection techniques are adopted to remove irrelevant, redundant and noisy features. Our approach focuses on retrieval of effective and efficient spatial clusters from large number of medical databases. In this chapter, we have defined our novel framework SpaGrid and SPAM algorithm to retrieve clusters of variant shape and size from large databases. The application of our framework is used with spatial medical databases where the implementation details are discussed with Matlab 7.1.
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