abbr.海牙仲裁公约(Hague Arbitration Convention)，海地空军；海地（陆军）航空兵(Haitian Air Corps)，航向校正圈（航天飞机）(Heading Alignment Circle)

HAC is an effective method for data preprocessing in machine learning.
HAC是机器学习中数据预处理的有效方法。
We applied HAC to cluster similar customer profiles for targeted marketing campaigns.
我们使用HAC对类似客户群进行聚类，以便进行目标市场营销活动。
Hierarchical Agglomerative Clustering (HAC) was used to group genes based on their expression patterns.
层次聚类（HAC）被用来根据基因的表达模式将它们分组。
The dendrogram generated by HAC helped us visualize the relationships between different species.
由HAC生成的树状图帮助我们可视化不同物种之间的关系。
HAC can handle large datasets with high computational efficiency, making it suitable for big data analysis.
HAC能够高效地处理大型数据集，适合用于大数据分析。
In our study, we compared HAC with K-means clustering and found that HAC performed better in identifying clusters with varying densities.
在我们的研究中，我们将HAC与K-means聚类进行了比较，发现HAC在识别密度不同的集群时表现更好。
To optimize the parameters of HAC, we conducted a grid search using cross-validation.
为了优化HAC的参数，我们使用交叉验证进行了网格搜索。
Using HAC, we were able to segment customers into distinct groups based on their purchasing behavior.
利用HAC，我们能够根据客户的购买行为将他们划分为不同的群体。
HAC has been successfully applied in image segmentation tasks, where pixels are grouped according to color or texture similarity.
HAC已成功应用于图像分割任务，其中像素根据颜色或纹理相似性进行分组。
The HAC algorithm allowed us to create a hierarchical structure of products based on their features, aiding in product categorization.
HAC算法使我们能够基于产品特征创建一个产品层次结构，有助于产品分类。