基于灰狼优化聚类算法的读者行为分析

通过分析国内外读者行为分析算法的优缺点，提出了一种基于灰狼优化聚类算法的读者行为分析算法.首先，通过灰狼优化算法自动寻优找到模糊C均值聚类算法的最佳初始聚类中心点，再对该初始聚类中心点进行迭代计算，得到最终聚类结果.读者借阅行为数据实验分析结果验证了灰狼优化聚类算法要优于普通的聚类算法.

Reader Behavior Analysis Based on Gray Wolf Optimization Clustering Algorithm

This paper proposes a reader behavior analysis algorithm based on a gray wolf optimization clustering algorithm through analyzing the advantages and disadvantages of current reader behavior analysis algorithms at home and abroad. First, the gray wolf optimization algorithm automatically optimizes to find the optimal initial cluster center point of the fuzzy C-means clustering algorithm. It then makes an iterative calculation of the initial cluster center point to obtain the final clustering result. Through the analysis of the reader borrowing behavior data, it is verified that the gray wolf optimization clustering algorithm is superior to the ordinary clustering algorithm.