和声模糊聚类在读者兴趣度建模中的应用

Applied of Harmony Fuzzy Clustering in Readers Interesting Degree Modeling

本文采取了3种必要的措施提高了聚类质量:考虑到各维数据特征属性对聚类效果影响不同,采用了基于统计方法的维度加权的方法进行特征选择;对于和声搜索算法的调音概率进行了改进,将改进的和声搜索算法和模糊聚类相结合用于快速寻找最优的聚类中心;循环测试各种中心数情况下的聚类质量以获得最佳的类中心数。接着,该算法被应用于图书馆读者兴趣度建模中,用于识别图书馆日常运行时各读者借阅图书的类型,实验表明该算法较其它算法更优。这样的读者兴趣度聚类分析可以进行图书推荐,从而提高图书馆的运行效率。

Three methods are adopted to achieve a better clustering quality in the paper.Firstly,considering the different influences of each dimension attribute of data on the clustering effect,statistic method is used to weight each dimension to select feature.Secondly,some improvements are carried out for the probability of harmony search algorithm and combine fuzzy clustering algorithm with harmony search to rapidly find the optimal cluster centers.Thirdly,iterative method is used to test clustering quality to get the best number of cluster center.Next,the proposed algorithm is applied to reader interesting degree model to distinguish and identify interesting degree category of various readers during library running.Experimental results show that the proposed clustering algorithm outperforms other similar algorithms.This readers interesting model can recommend reasonable books to appropriate readers and the operating efficiency of the library can be improved.