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Integrating Spatial Data Mining Technique to Identify Potential Landsat Data using K-Means and BPNN Algorithm

Spatial Data mining is one of the challenging field in data mining. The explosive development of spatial data and common use of spatial databases highlight the need for the automated detection of spatial knowledge. Computing data mining algorithms such as clustering on massive spatial data sets is still not feasible nor efficient today. In this research first we elaborate a study on data clustering, particularly on spatial data clustering. Here we introduce a k-means algorithm that is based on the data stream paradigm. Some of the existing classical clustering algorithm and the proposed BPNN were tested with UCI repository datasets for spatial data clustering and classification. Several tests were made on the system and overall significant results were achieved. Proposed method is an influential tool for the classification of multidimensional spatial data sets.