Parallel Spectral Clustering Algorithm Based on Hadoop

Spectral clustering and cloud computing is emerging branch of computer science or related discipline. It overcome the shortcomings of some traditional clustering algorithm and guarantee the convergence to the optimal solution, thus have to the widespread attention. This article first introduced the parallel spectral clustering algorithm research background and significance, and then to Hadoop the cloud computing Framework has carried on the detailed introduction, then has carried on the related to spectral clustering is introduced, then introduces the spectral clustering arithmetic Method of parallel and relevant steps, finally made the related experiments, and the experiment are summarized.

[1]  Inderjit S. Dhillon,et al.  Weighted Graph Cuts without Eigenvectors A Multilevel Approach , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Robert B. Ross,et al.  Using MPI-2: Advanced Features of the Message Passing Interface , 2003, CLUSTER.

[3]  Sridhar Mahadevan Fast Spectral Learning using Lanczos Eigenspace Projections , 2008, AAAI.

[4]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[6]  Lars George,et al.  HBase: The Definitive Guide , 2011 .