Multiview Clustering in Heterogeneous Environment

  • A. Bharathi
  • S. Anitha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 325)


Integrating multiview cluster is a crucial issue in heterogeneous environment. In multiview clustering, objects from the multiple sources can be limited up to homogeneous environment by sharing the same dimensions. In this paper, novel-based tensor methods are used. They are (1) multiview clustering based on the integration of the Frobenius-norm objective function (MC-FR-OI) and (2) matrix integration in the Frobenius-norm objective function (MC-FR-MI). These frameworks worked by using tensor decomposition. Experimental results demonstrate that proposed methods are effective in multiview data integration. Here, higher-order data are used. The performance by using higher-order data is better when compared with the two-dimensional data.


Integrating multiview cluster Frobenius-norm objective function Spectral clustering k-means SVD HOSVD 


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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Information TechnologySathyamangalam, ErodeIndia

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