Spectral Clustering Using Evolving Similarity Graphs

Christina Chrysouli, Anastasios Tefas


In this paper, we propose a novel spectral graph clustering method that uses evolutionary algorithms in order to optimise the structure of a graph, by using a fitness function, applied in clustering problems. Nearest neighbour graphs and variants of these graphs are used in order to form the initial population. These graphs are transformed in such a way so as to play the role of chromosomes in the evolutionary algorithm. Multiple techniques have been examined for the creation of the initial population, since it was observed that it plays an important role in the algorithm's performance. The advantage of our approach is that, although we emphasise in clustering applications, the algorithm may be applied to several other problems that can be modeled as graphs, including dimensionality reduction and classification. Experiments on traditional dance dataset and on other various multidimensional datasets were conducted using both internal and external clustering criteria as evaluation methods, which provided encouraging results.


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Paper Citation

in Harvard Style

Chrysouli C. and Tefas A. (2014). Spectral Clustering Using Evolving Similarity Graphs . In Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014) ISBN 978-989-758-052-9, pages 21-29. DOI: 10.5220/0005069200210029

in Bibtex Style

author={Christina Chrysouli and Anastasios Tefas},
title={Spectral Clustering Using Evolving Similarity Graphs},
booktitle={Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)},

in EndNote Style

JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications - Volume 1: ECTA, (IJCCI 2014)
TI - Spectral Clustering Using Evolving Similarity Graphs
SN - 978-989-758-052-9
AU - Chrysouli C.
AU - Tefas A.
PY - 2014
SP - 21
EP - 29
DO - 10.5220/0005069200210029