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

หน่วยงาน Nanyang Technological University, Singapore

รายละเอียด

ชื่อเรื่อง : Self-organizing topological tree for online vector quantization and data clustering
นักวิจัย : Xu, Pengfei , Chang, Chip Hong , Paplinski, Andrew P.
คำค้น : DRNTU::Engineering::Electrical and electronic engineering
หน่วยงาน : Nanyang Technological University, Singapore
ผู้ร่วมงาน : -
ปีพิมพ์ : 2548
อ้างอิง : 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. , 1083-4419 , http://hdl.handle.net/10220/6001 , http://dx.doi.org/10.1109/TSMCB.2005.846651
ที่มา : -
ความเชี่ยวชาญ : -
ความสัมพันธ์ : IEEE transactions on systems, man, and cybernetics-part B : cybernetics
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

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.

บรรณานุกรม :
Xu, Pengfei , Chang, Chip Hong , Paplinski, Andrew P. . (2548). Self-organizing topological tree for online vector quantization and data clustering.
    กรุงเทพมหานคร : Nanyang Technological University, Singapore.
Xu, Pengfei , Chang, Chip Hong , Paplinski, Andrew P. . 2548. "Self-organizing topological tree for online vector quantization and data clustering".
    กรุงเทพมหานคร : Nanyang Technological University, Singapore.
Xu, Pengfei , Chang, Chip Hong , Paplinski, Andrew P. . "Self-organizing topological tree for online vector quantization and data clustering."
    กรุงเทพมหานคร : Nanyang Technological University, Singapore, 2548. Print.
Xu, Pengfei , Chang, Chip Hong , Paplinski, Andrew P. . Self-organizing topological tree for online vector quantization and data clustering. กรุงเทพมหานคร : Nanyang Technological University, Singapore; 2548.