Literature DB >> 17063691

Dynamic characterization of cluster structures for robust and inductive support vector clustering.

Jaewook Lee1, Daewon Lee.   

Abstract

A topological and dynamical characterization of the cluster structures described by the support vector clustering is developed. It is shown that each cluster can be decomposed into its constituent basin level cells and can be naturally extended to an enlarged clustered domain, which serves as a basis for inductive clustering. A simplified weighted graph preserving the topological structure of the clusters is also constructed and is employed to develop a robust and inductive clustering algorithm. Simulation results are given to illustrate the robustness and effectiveness of the proposed method.

Mesh:

Year:  2006        PMID: 17063691     DOI: 10.1109/TPAMI.2006.225

Source DB:  PubMed          Journal:  IEEE Trans Pattern Anal Mach Intell        ISSN: 0098-5589            Impact factor:   6.226


  2 in total

1.  Stepwise iterative maximum likelihood clustering approach.

Authors:  Alok Sharma; Daichi Shigemizu; Keith A Boroevich; Yosvany López; Yoichiro Kamatani; Michiaki Kubo; Tatsuhiko Tsunoda
Journal:  BMC Bioinformatics       Date:  2016-08-24       Impact factor: 3.169

2.  2D-EM clustering approach for high-dimensional data through folding feature vectors.

Authors:  Alok Sharma; Piotr J Kamola; Tatsuhiko Tsunoda
Journal:  BMC Bioinformatics       Date:  2017-12-28       Impact factor: 3.169

  2 in total

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