Building and Assessing a Constrained Clustering Hierarchical Algorithm

Authors:
Eduardo R. Concepción Morales;Yosu Yurramendi Mendizabal
Affiliations:
Faculty of Informatics, University of Cienfuegos, Cuatro Caminos, Cuba;Department of Computer Science and Artificial Intelligence, University of the Basque Country/EHU, Donostia-San Sebastian, Spain
Venue:
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Year:
2008

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Abstract

Unsupervised classification or clustering has been used in many disciplines and contexts. Traditional methodologies are mostly based on the minimization of the distance between data and the cluster means without considering any other possible relationship present in data, e.g., spatial interactions. A constrained hierarchical agglomerative algorithm with an aggregation index is introduced which uses neighbouring relations present in the data. Experiments show the behaviour of the proposed constrained algorithm in different situations.