论文标题
使用MM原理处理K-均值集群中的不完整数据
Using MM principles to deal with incomplete data in K-means clustering
论文作者
论文摘要
在许多聚类算法中，由于其简单的算法和快速收敛，K-Means聚类算法被广泛使用。但是，该算法遭受了不完整的数据，其中一些样本错过了它们的某些属性。为了解决此问题，我们主要采用MM原理来恢复数据的对称性，以便K-均值可以很好地工作。我们给出算法的伪代码，并使用标准数据集进行实验验证。实验的源代码在以下链接中公开可用：\ url {https://github.com/alibeikmohammadi/mm---------------/blob/mini/mini/mini-project/mm%20k-means.ipynb}。
Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simple algorithm and fast convergence. However, this algorithm suffers from incomplete data, where some samples have missed some of their attributes. To solve this problem, we mainly apply MM principles to restore the symmetry of the data, so that K-means could work well. We give the pseudo-code of the algorithm and use the standard datasets for experimental verification. The source code for the experiments is publicly available in the following link: \url{https://github.com/AliBeikmohammadi/MM-Optimization/blob/main/mini-project/MM%20K-means.ipynb}.
