A clustering algorithm based on maximal θ-distant subtrees

Authors:
Li Yujian
Affiliations:
Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science and Technology, Beijing University of Technology, Beijing 100022, China
Venue:
Pattern Recognition
Year:
2007

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Abstract

This paper presents a clustering algorithm based on maximal @q-distant subtrees, the basic idea of which is to find a set of maximal @q-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.