数据分析与数据挖掘算法：K-means和层次聚类算法详解【英文版】

The document "数据分析与数据挖掘算法 kmeans算法介绍 K-均值与层次聚类算法 英文版 共24页.pdf" provides an introduction to the K-means and Hierarchical Clustering algorithms used in data analysis and data mining. These algorithms are essential in clustering data points and finding patterns within large datasets. The document introduces the K-means algorithm, which is used to partition n data points into k clusters, where each data point belongs to the cluster with the nearest mean. The algorithm iteratively assigns data points to the nearest cluster and recalculates the cluster means until the algorithm converges. Additionally, the document discusses Hierarchical Clustering, which is a method of cluster analysis that seeks to build a hierarchy of clusters. This algorithm does not require the user to specify the number of clusters beforehand. Instead, it creates a dendrogram that demonstrates the arrangement of the clusters and their subclusters. The document provides insights into how these algorithms can be utilized for various applications such as modeling Gaussian mixtures, lossy compression, and data transmission. It emphasizes the popular alternative of using K-means and Hierarchical Clustering for addressing these data-related challenges. Overall, the document serves as a comprehensive guide to understanding and implementing the K-means and Hierarchical Clustering algorithms in data analysis and data mining. It is a valuable resource for individuals and professionals seeking to delve into the field of cluster analysis and gain a deeper understanding of the algorithms' applications and capabilities.