Clustering Based on Dominant Set and Cluster Expansion

  • Jian Hou
  • Weixue Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10235)


While numerous clustering algorithms can be found in the literature, existing algorithms are usually afflicted by two major problems. First, the majority of clustering algorithms requires user-specified parameters as input, and their clustering results rely heavily on these parameters. Second, many algorithms generate clusters of only spherical shapes. In this paper we try to solve these two problems based on dominant set and cluster expansion. We firstly use a modified dominant sets clustering algorithm to generate initial clusters which are parameter independent and usually smaller than the real clusters. Then we expand the initial clusters based on two density based clustering algorithms to generate clusters of arbitrary shapes. In experiments on various datasets our algorithm outperforms the original dominant sets algorithm and several other algorithms. It is also shown to be effective in image segmentation experiments.


Cluster Center Cluster Result Arbitrary Shape Spectral Cluster Density Peak 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is supported in part by the National Natural Science Foundation of China under Grant No. 61473045 and by China Scholarship Council.


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© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.College of EngineeringBohai UniversityJinzhouChina
  2. 2.ECLTUniversità Ca’ Foscari VeneziaVeneziaItaly
  3. 3.College of Information ScienceBohai UniversityJinzhouChina

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