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Authors: Christina Chrysouli and Anastasios Tefas

Affiliation: Aristotle University of Thessaloniki, Greece

Keyword(s): Spectral Clustering, Similarity Graphs, Evolutionary Algorithms.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Genetic Algorithms ; Hybrid Systems ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Representation Techniques ; Soft Computing ; Symbolic Systems

Abstract: 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 meth ods, which provided encouraging results. (More)

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

@conference{ecta14,
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 (IJCCI 2014) - ECTA},
year={2014},
pages={21-29},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005069200210029},
isbn={978-989-758-052-9},
}

TY - CONF

JO - Proceedings of the International Conference on Evolutionary Computation Theory and Applications (IJCCI 2014) - ECTA
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
PB - SciTePress