利用改进的单纯形搜索改进K-means算法
Improving the K-means algorithm using improved downhill simplex search
摘要：
k-means算法是一种著名的和最流行的聚类算法。K-means通过迭代优化过程最小化平方误差之和来寻求数据的最佳分区，这属于爬山算法的范畴。众所周知，爬山搜索以收敛到局部最优而闻名。由于k-means可以收敛到局部最优，不同的初始点通常会导致不同的收敛cancroids，这使得从合理的初始划分开始以获得高质量的聚类解决方案变得非常重要。然而，在理论上，不存在用于确定这样的初始划分的有效且通用的方法。在本文中，我们试图找到一个最佳的初始划分的k-means算法。为了实现这一目标，我们提出了一个新的改进版本的下坡单纯形搜索，然后我们使用它，以找到一个最佳的结果，聚类方法，然后比较该算法与遗传算法基地（GA），遗传K-means（GKM），改进的遗传K-means（IGKM）和k-means算法。
英文摘要：
The k-means algorithm is one of the well-known and most popular clustering algorithms. K-means seeks an optimal partition of the data by minimizing the sum of squared error with an iterative optimization procedure, which belongs to the category of hill climbing algorithms. As we know hill climbing searches are famous for converging to local optimums. Since k-means can converge to a local optimum, different initial points generally lead to different convergence cancroids, which makes it important to start with a reasonable initial partition in order to achieve high quality clustering solutions. However, in theory, there exist no efficient and universal methods for determining such initial partitions. In this paper we tried to find an optimum initial partitioning for k-means algorithm. To achieve this goal we proposed a new improved version of downhill simplex search, and then we used it in order to find an optimal result for clustering approach and then compare this algorithm with Genetic Algorithm base (GA), Genetic K-Means (GKM), Improved Genetic K-Means (IGKM) and k-means algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
