凝集型クラスタリングは一般的な 階層型クラスタリング technique データ分析において使用される and 機械学習. It operates by initially treating each data point as a separate cluster and then progressively merging these clusters based on their similarity or distance to form larger clusters. This process continues until all data points are combined into a single cluster or until a specified number of clusters is reached.
この方法は通常、距離尺度を使用します。例えば ユークリッド距離, to measure the proximity between clusters. Common linkage criteria include single-linkage (minimum distance), complete-linkage (maximum distance), and average-linkage (mean distance), which determine how the distance between clusters is calculated during the merging process.
凝集型クラスタリングの利点の一つは、その能力です。 dendrogram, a tree-like diagram that illustrates the merging process and the relationships between clusters. This visual representation can help analysts understand the structure of the data and choose an appropriate number of clusters based on the desired granularity.
Despite its advantages, agglomerative clustering can be computationally intensive, especially for large datasets, as it requires calculating pairwise distances between clusters. Additionally, the choice of distance metric and linkage criteria can significantly affect the results, making it essential to select these parameters 注意深く。
Overall, agglomerative clustering is a versatile and widely-used technique in various applications, including market segmentation, 画像分類, and social network analysis.