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Evolutionary clustering framework based on distance matrix for arbitrary-shaped data sets

机译：基于距离矩阵的任意形状数据集进化聚类框架

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摘要

Data clustering plays a key role in both scientific and real-world applications. However, current clustering methods still face some challenges such as clustering arbitrary-shaped data sets and detecting the cluster number automatically. This study addresses the two challenges. A novel clustering analysis method, named automatic evolutionary clustering method based on distance (AED) matrix, is proposed to determine the proper cluster number automatically, and to find the optimal clustering result as well. In AED, a distance matrix is first obtained by using a specific distance metric such as Euclidean distance metric or path distance metric, and then this distance matrix is partitioned by an evolutionary clustering framework. In this framework, a fixed-length representation scheme is implemented to represent the clustering result, a novel cross-over scheme is introduced to increase the convergence speed, and a validity index is proposed to evaluate the intermediate clustering results and the final clustering results. AED is systematically compared with some state-of-the-art clustering methods on both hyper-spherical and irregular-shaped data sets, and the experimental results suggest that the authors approach not only successfully detects the correct cluster numbers but also achieves better accuracy for most of test problems.
机译：数据集群在科学和现实应用中都扮演着关键角色。但是，当前的聚类方法仍然面临一些挑战，例如聚类任意形状的数据集和自动检测聚类编号。这项研究解决了两个挑战。提出了一种新的聚类分析方法，即基于距离矩阵的自动进化聚类方法，可以自动确定合适的聚类数，并找到最佳的聚类结果。在AED中，首先通过使用特定的距离度量（例如欧几里得距离度量或路径距离度量）获得距离矩阵，然后通过进化聚类框架对该距离矩阵进行划分。在该框架下，采用定长表示方案表示聚类结果，提出了一种新的交叉方案以提高收敛速度，并提出了一种有效性指标来评价中间聚类结果和最终聚类结果。系统地比较了AED与超球形和不规则数据集上的一些最新聚类方法，实验结果表明，作者的方法不仅成功地检测出正确的聚类数，而且还获得了更好的精度。大多数测试问题。

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