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首页> 外文期刊>Expert Systems >Determination of the appropriate parameters for K-means clustering using selection of region clusters based on density DBSCAN (SRCD-DBSCAN)
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Determination of the appropriate parameters for K-means clustering using selection of region clusters based on density DBSCAN (SRCD-DBSCAN)

机译：使用基于密度DBSCAN（SRCD-DBSCAN）的区域聚类选择，确定适合K均值聚类的参数

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摘要

K-means clustering can be highly accurate when the number of clusters and the initial cluster centre are appropriate. An inappropriate determination of the number of clusters or the initial cluster centre decreases the accuracy of K-means clustering. However, determining these values is problematic. To solve these problems, we used density-based spatial clustering of application with noise (DBSCAN) because it does not require a predetermined number of clusters; however, it has some significant drawbacks. Using DBSCAN with high-dimensional data and data with potentially different densities decreases the accuracy to some degree. Therefore, the objective of this research is to improve the efficiency of DBSCAN through a selection of region clusters based on density DBSCAN to automatically find the appropriate number of clusters and initial cluster centres for K-means clustering. In the proposed method, DBSCAN is used to perform clustering and to select the appropriate clusters by considering the density of each cluster. Subsequently, the appropriate region data are chosen from the selected clusters. The experimental results yield the appropriate number of clusters and the appropriate initial cluster centres for K-means clustering. In addition, the results of the selection of region clusters based on density DBSCAN method are more accurate than those obtained by traditional methods, including DBSCAN and K-means and related methods such as Partitioning-based DBSCAN (PDBSCAN) and PDBSCAN by applying the Ant Clustering Algorithm DBSCAN (PACA-DBSCAN).
机译：当聚类的数量和初始聚类中心合适时，K均值聚类可以非常准确。聚类数量或初始聚类中心的不适当确定会降低K均值聚类的准确性。但是，确定这些值是有问题的。为了解决这些问题，我们使用了基于密度的带噪声的应用程序空间聚类（DBSCAN），因为它不需要预定数量的聚类。但是，它有一些明显的缺点。对高维数据和密度可能不同的数据使用DBSCAN会在一定程度上降低准确性。因此，本研究的目的是通过基于密度DBSCAN的区域聚类选择以自动找到合适数量的聚类和初始聚类中心来进行K均值聚类，从而提高DBSCAN的效率。在提出的方法中，DBSCAN用于执行聚类并通过考虑每个聚类的密度来选择适当的聚类。随后，从选定的群集中选择适当的区域数据。实验结果为K均值聚类产生了适当数量的聚类和适当的初始聚类中心。此外，基于密度DBSCAN方法的区域聚类选择结果比传统方法（包括DBSCAN和K-means以及相关方法，例如通过应用Ant的基于分区的DBSCAN（PDBSCAN）和PDBSCAN）获得的结果更准确。聚类算法DBSCAN（PACA-DBSCAN）。

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