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DBSCAN Clustering Algorithm Based on Density

机译：基于密度的DBSCAN聚类算法

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Clustering technology has important applications in data mining, pattern recognition, machine learning and other fields. However, with the explosive growth of data, traditional clustering algorithm is more and more difficult to meet the needs of big data analysis. How to improve the traditional clustering algorithm and ensure the quality and efficiency of clustering under the background of big data has become an important research topic of artificial intelligence and big data processing. The density-based clustering algorithm can cluster arbitrarily shaped data sets in the case of unknown data distribution. DBSCAN is a classical density-based clustering algorithm, which is widely used for data clustering analysis due to its simple and efficient characteristics. The purpose of this paper is to study DBSCAN clustering algorithm based on density. This paper first introduces the concept of DBSCAN algorithm, and then carries out performance tests on DBSCAN algorithm in three different data sets. By analyzing the experimental results, it can be concluded that DBSCAN algorithm has higher homogeneity and diversity when it performs personalized clustering on data sets of non-uniform density with broad values and gradually sparse forwards. When the DBSCAN algorithm's neighborhood distance eps is 1000, 26 classes are generated after clustering.
机译：聚类技术在数据挖掘，模式识别，机器学习和其他领域具有重要应用。然而，随着数据的爆炸性增长，传统的聚类算法越来越难以满足大数据分析的需求。如何提高传统聚类算法，确保大数据背景下集群的质量和效率已成为人工智能和大数据处理的重要研究课题。基于密度的聚类算法可以在未知数据分布的情况下群集任意形状的数据集。 DBSCAN是一种经典密度的聚类算法，其由于其简单高效的特性而广泛用于数据聚类分析。本文的目的是研究基于密度的DBSCAN聚类算法。本文首先介绍了DBSCAN算法的概念，然后在三种不同数据集中对DBSCAN算法进行性能测试。通过分析实验结果，可以得出结论，当DBSCAN算法在具有宽的值和逐渐稀疏前向前逐渐稀疏的正向上执行个性化聚类时，DBSCAN算法具有更高的均匀性和多样性。当DBSCAN算法的邻距离EPS为1000时，群集后生成26类。

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