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Due to the complexity of geoscientific data, such as geochemical data, geophysical data and digital remote sensing data, traditional data mining methods, such as cluster analysis and association analysis, have limitations in resources evaluation. In this paper, a clustering algorithm is presented which has the ability to handle clusters of arbitrary shapes, sizes and densities. For association analysis,...
For applications of clustering algorithms, the key techniques are to handle complicatedly distributed clusters and process massive data effectively and efficiently. On the basis of analysis and research of traditional clustering algorithms, a clustering algorithm based on density and adaptive density-reachable is presented in this paper, which can handle clusters of arbitrary shapes, sizes and densities...
Three clustering methods are presented and discussed by experimental analysis. The results by using three clustering methods which are partitioning methods, hierarchical methods and density-based methods visually illustrate the clustering results, in two-dimensional data sets as experimental data are used. Clearly, when the original data set is spherical shape, most of the cluster methods can get...
The traditional clustering algorithms are only suitable for the static datasets. As for the dynamic and incremental datasets, the clustering results will become unreliable after data updates, and also it will certainly decrease efficiency and waste computing resources to cluster all of the data again. To overcome these problems, a new incremental clustering algorithm is proposed on the basis of density...
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