改进的聚类分析算法及其性能分析

提出了一种改进的聚类分析算法，该算法采用类似中间聚类与最终聚类分布的思想，先对密集区域进行聚类，形成了K个聚类，然后再对相对分散的自由数据进行K—means聚类，使聚类分析在迭代过程中始终沿着最优的方向进行，减小了迭代次数，提高了收敛速度。该算法融合了网格聚类与K-均值聚类的优点，并且引入了一种新的划分网格的算法和新的计算密度阀值的函数。理论分析以及实验证明，改进算法的聚类过程达到了令人满意的效果。

Improved Clustering Analysis Algorithm and Its Performance Analysis

An improved clustering analysis algorithm is proposed.Using the idea similar to half-finished clustering and final clustering distribution,the algorithm firstly clusters concentrated regions to get K clusters,and then clusters relatively scattered free data in K-means,which makes clustering analysis always follow optimal direction in iterative process,reduces iteration times and improves convergence speed.The algorithm integrates the advantages of grid-based clustering and K-means clustering,and introduces ...