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Autonomous Data Density based clustering method

机译：基于自主数据密度的聚类方法

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It is well known that clustering is an unsupervised machine learning technique. However, most of the clustering methods need setting several parameters such as number of clusters, shape of clusters, or other user- or problem-specific parameters and thresholds. In this paper, we propose a new clustering approach which is fully autonomous, in the sense that it does not require parameters to be pre-defined. This approach is based on data density automatically derived from their mutual distribution in the data space. It is called ADD clustering (Autonomous Data Density based clustering). It is entirely based on the experimentally observable data and is free from restrictive prior assumptions. This new method exhibits highly accurate clustering performance. Its performance is compared on benchmarked data sets with other competitive alternative approaches. Experimental results demonstrate that ADD clustering significantly outperforms other clustering methods yet does not require restrictive user- or problem-specific parameters or assumptions. The new clustering method is a solid basis for further applications in the field of data analytics.
机译：众所周知，集群是一种无监督的机器学习技术。但是，大多数聚类方法都需要设置几个参数，例如聚类数量，聚类形状或其他特定于用户或问题的参数和阈值。在本文中，我们提出了一种完全自治的新聚类方法，因为它不需要预先定义参数。这种方法基于自动从它们在数据空间中的相互分布中得出的数据密度。这称为ADD群集（基于自主数据密度的群集）。它完全基于实验可观察的数据，并且没有限制性的先前假设。这种新方法展现出高度准确的聚类性能。在基准数据集上将其性能与其他竞争性替代方法进行了比较。实验结果表明，ADD聚类明显优于其他聚类方法，但不需要限制性的特定于用户或问题的参数或假设。新的群集方法为数据分析领域的进一步应用奠定了坚实的基础。

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