Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
531752 | Pattern Recognition | 2007 | 7 Pages |
Abstract
This paper presents a clustering algorithm based on maximal θθ-distant subtrees, the basic idea of which is to find a set of maximal θθ-distant subtrees by threshold cutting from a minimal spanning tree and merge each of their vertex sets into a cluster, coupled with a post-processing step for merging small clusters. The proposed algorithm can detect any number of well-separated clusters with any shapes and indicate the inherent hierarchical nature of the clusters present in a data set. Moreover, it is able to detect elements of small clusters as outliers in a data set and group them into a new cluster if the number of outliers is relatively large. Some computer simulations demonstrate the effectiveness of the clustering scheme.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Li Yujian,