elbow method

Elbow method is an unsupervised machine learning technique used for selecting the optimal number of clusters in a dataset. It involves plotting the within-cluster sum of squares (WSS) against the number of clusters and identifying the "elbow point" in the plot, which represents the point of diminishing returns in terms of reducing the WSS. The number of clusters corresponding to the elbow point is considered as the optimal number of clusters for the dataset.

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Elbow Method

Elbow Method is an unsupervised machine learning algorithm used to determine the optimal number of clusters in a dataset. It works by plotting the within-cluster sum of squares (WCSS) against the number of clusters, and identifying the "elbow" point in the graph where the rate of decrease in WCSS slows down. The number of clusters at the elbow point is considered to be the optimal number of clusters for the dataset.

Elbow method

Elbow method是一种常用于确定k值的方法，用于k-means聚类算法中。首先，我们需要选择一个k值，也就是我们希望把数据分成多少类。然后，通过计算不同k值下聚类结果的误差平方和（SSE）或者聚类间的方差（SSB）与k的函数关系，来判断k为多少时聚类效果最好。具体来说，在Elbow method中，我们绘制出k值和对应的误差平方和（或聚类间方差）的关系图，通常会形成一个弯曲的曲线。该曲线在k取某个值后会迅速下降，然后逐渐趋于平缓。这个拐点或者弯曲点被称为"elbow point"，因为它的形状类似手肘的弯曲点。我们可以选择这个elbow point对应的k值作为最佳的聚类数。