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With the wide application of GIS to all kinds of fields, and developing of the technique of data mining and spatial data collection, the technique of data mining in spatial database-spatial data mining is coming out. In order to satisfy the people's demand for the interesting and potentially useful knowledge from the spatial database, this thesis used a wide using spatial clustering algorithm: k-means...
Clustering is an important research topic in data mining that appears in a wide range of unsupervised classification applications. Partitional clustering algorithms such as the k-means algorithm are the most popular for clustering large datasets. The major problem with the k-means algorithm is that it is sensitive to the selection of the initial partitions and it may converge to local optima. In this...
Distributed clustering is a new research field of data mining now. In this paper, one of distributed clustering named DCBKC (distributed clustering based on K-means and coarse-grained parallel genetic algorithm) based on K-means and coarse-grained parallel genetic algorithm is advanced. The algorithm can solve local clustering problem of distributed clustering effectively, reflect all of local data...
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