Research Article
A Novel Hierarchical Clustering Algorithm Based on Density Peaks for Complex Datasets
Require: dataset , neighborhood , child threshold . | 1: Compute -nearest density and distance for each point | 2: Generate a tree by connecting from one point to its nearest point with higher density, and assign the whole tree as a single cluster. | 3: Sort all the edges of the tree with respect to the weights in descending order. | 4: repeat | 5: Remove the highest edge(s) in (in case of same weights, edges must | be cut simultaneously) to get subtrees | 6: for do | 7: if children of then | 8: All children nodes in this subtree are assigned as “noise”. | 9: else | 10: assign a new cluster to subtree | 11: end if | 12: end for | 13: until some stopping condition is satisfied. |
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