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在现有图书借阅数据的基础上,对图书馆进行主题挖掘,来应对主动服务读者的要求。为减少主观因素对数据分析的影响,提高分析质量,采用传统K均值算法对图书馆主题挖掘是一种常用方法,但该算法本身存在一些固有的缺陷。为了改善图书馆主题挖掘效果,提出了一种基于K均值的改进算法。文章采用南通纺织职业技术学院1年的图书借阅数据对该算法和K均值算法进行了主题挖掘实验。结果表明,该算法在聚类准确度和收敛速度方面,相比K均值算法效果更好,聚类结果也更为合理。
On the basis of borrowing data of existing books, the library carries on the theme excavation to deal with the requests of active service readers. In order to reduce the impact of subjective factors on data analysis and improve the quality of analysis, the traditional K-means algorithm is a common method for library theme mining. However, the algorithm itself has some inherent defects. In order to improve the library theme mining effect, an improved algorithm based on K-means is proposed. The article uses Nantong Textile Vocational and Technical College 1 year of book loan data to carry out the theme mining experiment and K-means algorithm. The results show that compared with K-means algorithm, the proposed algorithm is more effective in clustering accuracy and convergence rate and the clustering result is more reasonable.