were used as clustering algorithms. For determining which variables are significant distinctions between the generated clusters one way ANOVA was used for numerical variables and Tukey was used for post-hoc test. Similarly,the relationships between categorical variables and clusters were investi...
Finally, with the help of structural IGs and granular rules, a rule-based modeling method is constructed in the output space for online clustering. Experiments on a synthetic toy dataset and a typical spatial dataset are implemented in this paper. Numerical results validate the feasibility to the...
DBSCAN is an unsupervised density-based machine learning clustering algorithm that finds clusters of arbitrary shape in the presence of noisy points and has a wider range of applications than K-means, another common clustering algorithm, for example, for the implementation of advanced systems, such ...
In the clustering process, the point-traversal order of each point was recorded. The cluster with the largest number of points was chosen as the major tree point cloud and the other clusters and noise were removed. 2.3.2. Tree Slice and Segment Generation In this study, the tree skeleton ...
Moreover, these clusters demonstrate the occurrence of cross-regional clustering, alongside irregular shapes. For example, the Yangtze River Delta shows a V-type cluster, which extends to two adjacent provinces through Shanghai. The Pearl River Delta, on the other hand, is a △-type cluster, ...