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A K-Means Optimization Algorithm Based on Relative Core Cluster

机译:基于相对核心簇的K-均值优化算法

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With the rapid development of the technology of cluster analysis, people have proposed a lot of clustering algorithms, such as the K-means clustering algorithm which is simple, low complexity and has been used widely, and it has been the improved object or base for many other algorithms. This paper presents a K-means optimization algorithm based on relative core cluster -RCBK-means. The algorithm is based on the core group, uses the center of the relative core cluster of the data set as the initial center of the K-means algorithm, thus avoiding the local optimization problem of the clustering results which caused by selecting the initial center randomly of the classic K-means algorithm, and improving the algorithm results effectively.
机译:随着聚类分析技术的飞速发展,人们提出了许多聚类算法,例如简单,低复杂度,被广泛使用的K-means聚类算法,已经成为聚类分析的改进对象或基础。许多其他算法。提出了一种基于相对核心簇RCBK-means的K-means优化算法。该算法基于核心群,使用数据集的相对核心簇的中心作为K-means算法的初始中心,从而避免了因随机选择初始中心而导致的聚类结果的局部优化问题。改进了经典的K均值算法,有效地提高了算法效果。

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