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Clustering Social Tags with Improved DBSCAN Algorithm |
Xiong Huixiang(), Ye Jiaxin, Jiang Wuxuan |
School of Information Management, Central China Normal University, Wuhan 430079, China |
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Abstract [Objective] This paper tries to improve the DBSCAN algorithm and verify its feasibility and effectiveness in social tagging. [Methods] First, we analyzed the frequency of social tags for resources and their total appearances. Then, we examined the relationship between tags and resources to improve the DBSCAN clustering algorithm. Finally, we applied the new algorithm to cluster tags, and users. [Results] We ran our experiment with data from Douban Movies. The modified DBSCAN algorithm improved the inter-object and inter-cluster correlations of social taggings. [Limitations] The sample datasets need more in-depth mining. [Conclusions] The improved DBSCAN algorithm could effectively cluster social tags.
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Received: 30 March 2018
Published: 16 January 2019
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