论文标题
将聚类作为不良问题求解：使用K均值算法的实验
Solving clustering as ill-posed problem: experiments with K-Means algorithm
论文作者
论文摘要
在此贡献中，将基于K均值算法的聚类程序作为一个反问题进行了研究，这是问题不足的特殊情况。通过主组件分析（PCA）来改善聚类逆问题驱动器质量以减少输入数据的尝试。由于存在丁的定理，并且将发现的最佳簇的基数与K均值和所选信息丰富的PCA组件的基数联系起来，因此计算实验测试了两个定量特征选择方法之间的定理：Kaiser标准（基于不良决策），而基于“ QuistArt Deciess）与基于随机的Matrix理论）。结果表明，通过WishArt标准选择特征的PCA降低会导致矩阵条件数较低，并满足簇和组件之间的关系由定理预测。用于计算的数据来自神经科学的存储库：它对执行了面向任务的功能磁共振成像（fMRI）范式的健康和年轻受试者。
In this contribution, the clustering procedure based on K-Means algorithm is studied as an inverse problem, which is a special case of the illposed problems. The attempts to improve the quality of the clustering inverse problem drive to reduce the input data via Principal Component Analysis (PCA). Since there exists a theorem by Ding and He that links the cardinality of the optimal clusters found with K-Means and the cardinality of the selected informative PCA components, the computational experiments tested the theorem between two quantitative features selection methods: Kaiser criteria (based on imperative decision) versus Wishart criteria (based on random matrix theory). The results suggested that PCA reduction with features selection by Wishart criteria leads to a low matrix condition number and satisfies the relation between clusters and components predicts by the theorem. The data used for the computations are from a neuroscientific repository: it regards healthy and young subjects that performed a task-oriented functional Magnetic Resonance Imaging (fMRI) paradigm.
