The Elbow Method is a widely used heuristic for selecting the optimal number of clusters in a clustering algorithm, particularly in k-meansクラスタリング. The method involves plotting the explained variance against the number of clusters and identifying the point at which the addition of more clusters yields diminishing returns, resembling an ‘elbow’ shape.
エルボー法を実施するには、次の手順に従います。
- クラスタリングを実行 分析: Apply a clustering algorithm (e.g., k-means) to the dataset クラスタ数（k）の範囲で
- 慣性を計算： For each value of k, calculate the inertia, which is the sum of squared distances between data points and their assigned cluster centroid. Inertia measures how tightly the clusters are packed.
- 慣性をプロット： Create a plot with the number of clusters on the x-axis and the inertia on the y-axis.
- 肘のポイントを特定： Look for the point where the inertia begins to decrease at a slower rate. This point is considered the optimal number of clusters.
The Elbow Method provides a visual way to assess the trade-off between the number of clusters and the quality of the clustering, helping analysts make informed decisions about how to segment their data effectively. However, it is important to note that the Elbow Method is somewhat subjective, as the ‘elbow’ point may not always be clear, and multiple methods may be used to validate the choice of clusters.