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Self-organizing topological tree for online vector quantization and data clustering

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Self-organizing topological tree for online vector quantization and data clustering

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dc.contributor.author Xu, Pengfei
dc.contributor.author Chang, Chip Hong
dc.contributor.author Paplinski, Andrew P.
dc.date.accessioned 2009-08-03T03:31:32Z
dc.date.available 2009-08-03T03:31:32Z
dc.date.copyright 2005
dc.date.issued 2009-08-03T03:31:32Z
dc.identifier.citation Xu, P., Chang, C. H., & Paplinski, A. (2005). Self-organizing topological tree for online vector quantization and data clustering. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 35(3), 515-526.
dc.identifier.issn 1083-4419
dc.identifier.uri http://hdl.handle.net/10220/6001
dc.description.abstract The self-organizing Maps (SOM) introduced by Kohonen implement two important operations: vector quantization (VQ) and a topology-preserving mapping. In this paper, an online self-organizing topological tree (SOTT) with faster learning is proposed. A new learning rule delivers the efficiency and topology preservation, which is superior of other structures of SOMs. The computational complexity of the proposed SOTT is O(logN ) rather than O(N) as for the basic SOM. The experimental results demonstrate that the reconstruction performance of SOTT is comparable to the full-search SOM and its computation time is much shorter than the full-search SOM and other vector quantizers. In addition, SOTT delivers the hierarchical mapping of codevectors and the progressive transmission and decoding property, which are rarely supported by other vector quantizers at the same time. To circumvent the shortcomings of clustering performance of classical partition clustering algorithms, a hybrid clustering algorithm that fully exploit the online learning and multiresolution characteristics of SOTT is devised. A new linkage metric is proposed which can be updated online to accelerate the time consuming agglomerative hierarchical clustering stage. Besides the enhanced clustering performance, due to the online learning capability, the memory requirement of the proposed SOTT hybrid clustering algorithm is independent of the size of the data set, making it attractive for large database.
dc.format.extent 12 p.
dc.language.iso en
dc.relation.ispartofseries IEEE transactions on systems, man, and cybernetics-part B : cybernetics
dc.rights IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics © 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. http://www.ieee.org/portal/site.
dc.subject DRNTU::Engineering::Electrical and electronic engineering
dc.title Self-organizing topological tree for online vector quantization and data clustering
dc.type Journal Article
dc.contributor.school School of Electrical and Electronic Engineering
dc.identifier.doi http://dx.doi.org/10.1109/TSMCB.2005.846651
dc.description.version Published version

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