自更新过程聚类算法的优点分析
On the strengths of the self-updating process clustering algorithm
摘要：
我们介绍了一个简单，直观，但功能强大的聚类分析算法。该算法是一个在样本空间上的迭代过程，它是迭代生成的相关矩阵的扩展。它允许时变和时不变算子，因此可以被认为比模糊均值漂移算法更一般，在模糊均值漂移算法中算子是时不变的。该算法从数据点的角度出发，模拟数据点移动和自聚类的过程，因此称为自更新过程。它对于（i）具有噪声的数据，（ii）具有大量聚类的数据和（iii）不平衡的数据特别具有竞争力。当数据中存在噪声时，该算法能够隔离噪声点，同时执行聚类。仿真研究和实际数据的应用，以证明的性能。
英文摘要：
We introduce a simple, intuitive and yet powerful algorithm for clustering analysis. This algorithm is an iterative process on the sample space, which arises as an extension of the iteratively generated correlation matrices. It allows for both time-varying and time-invariant operators, therefore can be considered more general than the blurring mean-shift algorithm in which operators are time-invariant. The algorithm stands from the viewpoint of data points and simulates the process how data points move and perform self-clustering, therefore is named Self-Updating Process (SUP). It is particularly competitive for (i) data with noise, (ii) data with large number of clusters and (iii) unbalanced data. When noise is present in the data, the algorithm is able to isolate noisy points while performing clustering simultaneously. Simulation studies and real data applications are presented to demonstrate the performance of SUP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
