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New models for the clustering of large databases through a hierarchical paradigm

Authors: Irene Luque Ruiz; Gonzalo Cerruela García; Manuel Urbano-Cuadrado; Miguel Ángel Gómez-Nieto;

New models for the clustering of large databases through a hierarchical paradigm

Abstract

The recovery of information from large databases based on similarity approach supposes a high computational cost -when the process is carried out comparing each one of the records with the search pattern. If the database records store some data structure representing the information of the problem domain by means of a graph it is possible to classify these records using a hierarchical model which considers the structural basic elements of the graphs and diminishes the computational cost of the recovery process considerably. In this paper we propose a classification model based on structural elements (cycles and chains) for large and medium databases.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
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