计算机科学 ›› 2012, Vol. 39 ›› Issue (Z6): 283-287.

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用子空间粒子群聚类算法识别Folksonomy标签冗余的研究

王晓帅,覃华,丁立朵,马翩翩   

  1. (广西大学计算机与电子信息学院 南宁 530004)
  • 出版日期:2018-11-16 发布日期:2018-11-16

Particle Swarm Optimization for Subspace Clustering Identi行Tag Redundancy in Folksonomy

  • Online:2018-11-16 Published:2018-11-16

摘要: Wcb2.。标签系统中经常包含很多冗余的标签,标签冗余会增加用户选择喜好项目时的负担,从而影响用户建模和对推荐系统的评估。标签数据集通常存在着大量不相关或是冗余的特征,而不同簇之间的相关特征子集又是不一样的,所以应该从不同的特征子集中来发现簇。提出使用子空间粒子群聚类识别标签冗余,算法采用指数型变权类似K-means的目标函数,该函数对变量权值的改变更加敏感。在此基础上利用粒子群优化目标函数搜寻得到全局最优的标签聚类,提高抽取冗余标签的准确度实验结果表明,此算法具有较强的全局搜索能力,应用于标签冗余识别获得了更好的精度。

关键词: Web2.0标签推荐系统,标签冗余,子空间粒子群聚类

Abstract: Web2. 0 tag recommender systems always contain a lot of redundant special label. Redundancy on tags may even burden the user with additional effort by selecting their preferred items, and these redundancy can introduce erroneous features into the user profile and hamper the effort to judge recommendations. There is usually a lot of irrelevant or redundant features in high-dimensional data sets, feature subsets between different clusters are not the same. Therefore,we should focus on the different features subsets to discover the cluster. This paper proposed subspace PSO clustering to identify tag redundancy. We designed a suitable weighting K-means o均ective function, which is more sensitive to the change variables in weight. On this basis we developed PSO to optimize the objective function, then obtain global optimal value, and finally improve tag redundancy accuracy. Our experimental results show that the proposed algorithm has greater searching capability and obtains a better clustering accuracy.

Key words: Web2. 0 tag recommender systems, Tag redundancy,Subspace PSO clustering

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