Clustering techniques are widely used in many applications. The goal of clustering is to identify patterns or groups of similar objects within a dataset of interest. However, many cluster methods are neither robust nor sensitive to noises and outliers in real data. In this paper, we present Nuclear Norm Clustering (NNC, available at https://sourceforge.net/projects/nnc/), an algorithm that can be used in various fields as a promising alternative to the k-means clustering method. The NNC algorithm requires users to provide a data matrix M and a desired number of cluster K. We employed simulated annealing techniques to choose an optimal label vector that minimizes nuclear norm of the pooled within cluster residual matrix. To evaluate the performance of the NNC algorithm, we compared the performance of both 15 public datasets and 2 genome-wide association studies (GWAS) on psoriasis, comparing our method with other classic methods. The results indicate that NNC method has a competitive performance in terms of F-score on 15 benchmarked public datasets and 2 psoriasis GWAS datasets. So NNC is a promising alternative method for clustering tasks.

中文翻译：

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核规范聚类：一种有希望的聚类任务替代方法。

群集技术已广泛用于许多应用程序中。聚类的目的是识别感兴趣的数据集中的相似对象的模式或组。但是，许多聚类方法既不健壮，也不对实际数据中的噪声和异常值敏感。在本文中，我们介绍了核规范聚类（NNC，可从https://sourceforge.net/projects/nnc/获得），该算法可在各种领域中用作k均值聚类方法的有希望的替代方法。NNC算法要求用户提供数据矩阵M和所需数目的聚类K。我们采用模拟退火技术来选择最佳标记向量，以使聚类残差矩阵中合并的核范数最小。为了评估NNC算法的性能，我们比较了15种公共数据集和2种全基因组关联研究在牛皮癣上的表现，并将我们的方法与其他经典方法进行了比较。结果表明，NNC方法在15个基准公共数据集和2个牛皮癣GWAS数据集的F分数方面具有竞争优势。因此，NNC是一种有希望的集群任务替代方法。