Parallel Spectral Clustering Algorithm Based on Hadoop
This work addresses the need for faster clustering in big data applications, though it is incremental as it applies an existing method to a new framework.
The paper tackled the challenge of scaling spectral clustering to large datasets by implementing a parallel version on the Hadoop cloud computing framework, achieving improved computational efficiency through distributed processing.
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.