基于动态离差平方和准则的无监督机器学习

Unsupervised Machine Learning Based on the Sum of Squares of the Dynamic Deviations

无监督机器学习算法中的经典K-means算法,是基于局部最优的原则将观测数据进行分类,其算法严重依赖初始种子的选取,导致聚类结果的不确定性。基于经典K-means算法的不足,提出一种基于动态离差平方和的无监督机器学习算法,使得各类内观测值的离差平方和最小,以此为划分依据,不断调整,从而对K-means算法进行改进。

The classical K-means algorithm in unsupervised machine learning algorithms classifies observation data based on the principle of local optimization.The algorithm relies heavily on the selection of initial seeds,which leads to the uncertainty of clustering results.Based on the shortcomings of the classical K-means algorithm,an unsupervised machine learning algorithm based on the sum of squares of dynamic dispersions is proposed,which minimizes the sum of squared deviations of various types of internal observations.