A Resource-tag Attribute Graph Clustering Algorithm
نویسندگان
چکیده
In order to improve the effectiveness of clustering, a resource-tag attribute graph clustering algorithm is proposed in this paper, which makes a two-stage of clustering by the use of attribute tags of nodes in graphs. Firstly tags clusters are generated based on the weight and similarity of tags, which is the preprocessing of tags. Secondly the attribute vectors of nodes in resources are tested and calculated and then according to the preprogrammed threshold value the corresponding clustering sub-graphs join in order to increase the contact edge of nodes. Finally a related comparative test is made between fuzzy clustering algorithm, k-means algorithm and algorithm proposed in this paper. The data is a top250 data set from Douban reading. It turns out that in this algorithm F value can achieve much high and clustering entropy tends to zero. The effectiveness and accuracy of the algorithm are verified.
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