Zarrazola Rivera, EdwinMontero De Juan, Francisco JavierYáñez Gestoso, Francisco JavierGómez De Castro, Ana Inés2023-06-202023-06-202011978-1-4577-1676-810.1109/ISDA.2011.6121754https://hdl.handle.net/20.500.14352/45541In this paper we propose an efficient and polynomial hierarchical clustering technique for unsupervised classification of items being connected by a graph. The output of this algorithm shows the cluster evolution in a divisive way, in such a way that s soon as two items are included in the same cluster they will join a common cluster until the last iteration, in which all the items belong to a singleton cluster. This output can be viewed as a fuzzy clustering in which for each alpha cut we have a standard cluster of the network. The clustering tool we present in this paper allows a hierarchical clustering of related items avoiding some unrealistic constraints that are quite often assumed in clustering problems. The proposed procedure is applied to a hierarchical segmentation problem in astronomical images.engbook parthttps//doi.org/10.1109/ISDA.2011.6121754http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=6121754&abstractAccess=no&userType=instrestricted access519.8Graph TheoryHierarchical ClusteringAstronomical Images.Investigación operativa (Matemáticas)1207 Investigación Operativa