Spectral clustering algorithm has proved be more effective than most traditional algorithms in finding clusters.\r\nHowever, its high computational complexity limits its effect in actual application. This paper combines the spectral\r\nclustering with MapReduce, through evaluation of sparse matrix eigenvalue and computation of distributed cluster,\r\nputs forward the improvement ideas and concrete realization, and thus improves the clustering speed of the\r\ndistinctive clustering algorithm. According to the experiment, with the processing data scale being enlarged, the\r\nclustering rate is in nearly linear growth, and the proposed parallel spectral clustering algorithm is suitable for large\r\ndata mining. The research results provide research basis to better design a clustering partition algorithm in large\r\ndata and high efficiency.
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