Journal
KNOWLEDGE-BASED SYSTEMS
Volume 74, Issue -, Pages 69-88Publisher
ELSEVIER
DOI: 10.1016/j.knosys.2014.11.010
Keywords
Credal partition; Belief function theory; Median clustering; Community detection; Imprecise communities
Categories
Funding
- National Natural Science Foundation of China [61135001, 61403310]
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Median clustering is of great value for partitioning relational data. In this paper, a new prototype-based clustering method, called Median Evidential C-Means (MECM), which is an extension of median c-means and median fuzzy c-means on the theoretical framework of belief functions is proposed. The median variant relaxes the restriction of a metric space embedding for the objects but constrains the prototypes to be in the original data set. Due to these properties, MECM could be applied to graph clustering problems. A community detection scheme for social networks based on MECM is investigated and the obtained credal partitions of graphs, which are more refined than crisp and fuzzy ones, enable us to have a better understanding of the graph structures. An initial prototype-selection scheme based on evidential semicentrality is presented to avoid local premature convergence and an evidential modularity function is defined to choose the optimal number of communities. Finally, experiments in synthetic and real data sets illustrate the performance of MECM and show its difference to other methods. (C) 2014 Elsevier B.V. All rights reserved.
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