ML-Hierarchical Clustering

Produce a nested sequence of clusters, a tree, also called Dendrogram.

Type of Hierarchical Clustering

Agglomerative (bottom up) clustering: It builds the dendrogram (tree) from the bottom level, and

• merges the most similar (or nearest) pair of clusters
• stops when all the data points are merged into a single cluster (i.e., the root cluster).

Divisive (top down) clustering: It starts with all data points in one cluster, the root.

• Splits the root into a set of child clusters. Each child cluster is recursively divided further
• stops when only singleton clusters of individual data points remain, i.e., each cluster with only a single point

Pros and Cons

Pros:

• 实现简单，容易理解

Cons:

• 合并点/分裂点选择不太容易
• 合并/分裂的操作不能进行撤销
• 大数据集不太合适
• 执行效率较低$O(T{N}^2)$， $T$为迭代次数， $N$为样本点数