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首页> 外文期刊>IEEE transactions on industrial informatics >A Fast Density and Grid Based Clustering Method for Data With Arbitrary Shapes and Noise
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A Fast Density and Grid Based Clustering Method for Data With Arbitrary Shapes and Noise

机译：具有任意形状和噪声的数据的一种基于密度和网格的快速聚类方法

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摘要

This paper presents a density- and grid- based (DGB) clustering method for categorizing data with arbitrary shapes and noise. As most of the conventional clustering approaches work only with round-shaped clusters, other methods are needed to be explored to proceed classification of clusters with arbitrary shapes. Clustering approach by fast search and find of density peaks and density-based spatial clustering of applications with noise, and so many other methods are reported to be capable of completing this task but are limited by their computation time of mutual distances between points or patterns. Without the calculation of mutual distances, this paper presents an alternative method to fulfill clustering of data with any shape and noise even faster and with more efficiency. It was successfully verified in clustering industrial data (e.g., DNA microarray data) and several benchmark datasets with different kinds of noise. It turned out that the proposed DGB clustering method is more efficient and faster in clustering datasets with any shape than the conventional methods.
机译：本文提出了一种基于密度和网格的（DGB）聚类方法，可以对具有任意形状和噪声的数据进行分类。由于大多数常规聚类方法仅适用于圆形聚类，因此需要探索其他方法以对具有任意形状的聚类进行分类。通过快速搜索和找到密度峰值的聚类方法以及基于噪声的应用程序基于密度的空间聚类，据报道，许多其他方法能够完成此任务，但受点或模式之间相互距离的计算时间限制。在不计算相互距离的情况下，本文提出了另一种方法，可以更快，更高效地完成具有任何形状和噪声的数据聚类。它已成功地在聚类工业数据（例如DNA微阵列数据）和具有不同类型噪声的几个基准数据集中得到验证。事实证明，与传统方法相比，所提出的DGB聚类方法在对任何形状的数据集进行聚类时效率更高，速度更快。

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