Improved Algorithm of Hypergraph Clustering Based on Attributes Similarity
Clustering can be applied to many aspects of modern life,while data objects in modern life are often high dimensional and sparse. For this kind of high-dimensional data,the traditional clustering algorithm cannot be effectively dealt with. This paper proposes a improved algorithm of hypergraph clustering based on attribute similarity, on the basis of the original algorithm of hypergraph clustering, According to the threshold-edge distance form the hypergraph model and the hypergraph partitioning method to cluster the data object,using cluster-heads singular eigenvalue to evaluate the quality of clustering.
NETL
NSTL
万方数据
维普
