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The density-based agglomerative information bottleneck
The Information Bottleneck method aims to extract a compact representation which preserves the maximum relevant information. The sub-optimality in agglomerative Information Bottleneck (aIB) algorithm restricts the applications of Information Bottleneck method. In this paper, the concept of density-based chains is adopted to evaluate the information loss among the neighbors of an element, rather than the information loss between pairs of elements. The DaIB algorithm is then presented to alleviate the sub-optimality problem in aIB while simultaneously keeping the useful hierarchical clustering tree-structure. The experiment results on the benchmark data sets show that the DaIB algorithm can get more relevant information and higher precision than aIB algorithm, and the paired t-test indicates that these improvements are statistically significant.
History
Journal
Lecture notes in computer scienceVolume
5351Pagination
333 - 344Publisher
SpringerLocation
Berlin, GermanyPublisher DOI
ISSN
0302-9743eISSN
1611-3349Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2008, Springer-Verlag Berlin HeidelbergUsage metrics
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