The density connectivity information bottleneck
Contribuinte(s) |
Wang, Guojun Chen, Jianer Fellows, Michael R. Ma, Huadong |
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Data(s) |
01/01/2008
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Resumo |
Clustering with the agglomerative Information Bottleneck (aIB) algorithm suffers from the sub-optimality problem, which cannot guarantee to preserve as much relative information as possible. To handle this problem, we introduce a density connectivity chain, by which we consider not only the information between two data elements, but also the information among the neighbors of a data element. Based on this idea, we propose DCIB, a Density Connectivity Information Bottleneck algorithm that applies the Information Bottleneck method to quantify the relative information during the clustering procedure. As a hierarchical algorithm, the DCIB algorithm produces a pruned clustering tree-structure and gets clustering results in different sizes in a single execution. The experiment results in the documentation clustering indicate that the DCIB algorithm can preserve more relative information and achieve higher precision than the aIB algorithm.<br /> |
Identificador | |
Idioma(s) |
eng |
Publicador |
IEEE Computer Society |
Relação |
http://dro.deakin.edu.au/eserv/DU:30018140/li-densityconnectivity-2008.pdf http://doi.ieeecomputersociety.org/10.1109/ICYCS.2008.275 |
Direitos |
2008, IEEE |
Palavras-Chave | #the aIB algorithm #density connectivity #clustering tree-structure |
Tipo |
Conference Paper |