基于谱聚类的全局中心快速更新聚类算法

针对高维数据在聚类过程中存在迭代次数多、运算耗时长等问题，提出一种改进的聚类算法，首先采用谱聚类对样本降维，再选取k个首尾相连且距离乘积最大的数据对象作为初始聚类中心，在簇中心更新过程中，选取与簇均值距离最近的数据对象作为簇中心，并将其他数据对象按最小距离划分至相应簇中,反复迭代，直至收敛。实验结果表明，新算法的Rand指数、Jaccard系数和Adjusted Rand Index等聚类指标全部优于K-means算法及其他3种改进聚类算法，在运行效率方面，新算法的聚类耗时更短、迭代次数更少。

Global Center Fast Update Clustering Algorithm Based on Spectral Clustering

Aiming at the problems of high iteration number and long computation time in the clustering process of high dimensional data, an improved clustering algorithm is proposed. The algorithm first uses spectral clustering to reduce the dimension of samples, then selects k data objects with the end to end and the largest distance product as the initial clustering center, in the update process of cluster centers, selects data objects nearest to cluster mean as cluster centers. And other data objects are divided into corresponding clusters according to the minimum distance, iterated iteratively until convergence. The experimental results show that the Rand index, Jaccard coefficient and Adjusted Rand Index of  the new algorithm are better than K-means algorithm and other 3 kinds of improved clustering algorithms. In terms of operational efficiency, the new algorithm has shorter clustering time and fewer iterations.