li-densityconnectivity-2008.pdf (505.25 kB)
The density connectivity information bottleneck
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.
History
Event
International Conference for Young Computer Scientists (9th : 2008 : Zhang Jie Jie, China)Pagination
1783 - 1788Publisher
IEEE Computer SocietyLocation
Zhang Jia Jie, ChinaPlace of publication
Piscataway, N.J.Start date
2008-11-18End date
2008-11-21ISBN-13
9780769533988Language
engNotes
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E1 Full written paper - refereedCopyright notice
2008, IEEEEditor/Contributor(s)
G Wang, J Chen, M Fellows, H MaTitle of proceedings
Proceedings of the 9th International Conference for Young Computer ScientistsUsage metrics
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