Application of hierarchical clustering in machine learning

Hierarchical clustering is an unsupervised learning method used to group objects in a data set based on similarity. This method works by progressively dividing the data set into smaller and smaller subsets, eventually forming a hierarchical structure where each subset can be viewed as a cluster. Hierarchical clustering includes two types: agglomerative and divisive. Agglomerative hierarchical clustering starts with each object as an initial cluster and then gradually merges similar clusters until all objects are merged into one cluster. Schizoidal hierarchical clustering starts with the entire data set as an initial cluster and then gradually splits the cluster into smaller clusters until each object forms a separate cluster. Hierarchical clustering methods provide flexibility regarding the number of clusters while also being able to capture A point serves as a separate starting point for clustering, and clusters with high similarity are gradually merged to form large clusters or reach the required number of clusters. This method has the advantage of adapting to clusters of arbitrary shapes and does not require the number of clusters to be specified in advance. However, it is very sensitive to noise and outliers and suffers from high computational complexity. Therefore, when applying agglomerative hierarchical clustering, the data needs to be preprocessed to remove noise and outliers, while attention should be paid to the consumption of computing resources.

Schizoidal hierarchical clustering is a top-down method that achieves clustering by gradually dividing the entire data set into smaller and smaller subsets. It has the advantages of being insensitive to noise and outliers and having low computational complexity. However, the disadvantage of schizotypal hierarchical clustering is that it cannot adapt to clusters of arbitrary shapes and requires the number of clusters to be specified in advance.

The core of hierarchical clustering is similarity measurement. Common measurement methods include Euclidean distance, Manhattan distance and cosine similarity. These measures are used in the clustering process to calculate the distance or similarity between clusters to determine the merging or partitioning of clusters. Hierarchical clustering builds a clustering hierarchy by continuously merging or dividing clusters, with each level representing a different number of clusters.

The main steps of the hierarchical clustering algorithm include:

1. Calculate the distance or similarity matrix between samples.

2. Treat each sample as a cluster and build an initial clustering tree.

3. Repeat the following steps until a cluster is formed:

a. Calculate the distance between all clusters on the current clustering tree distance or similarity.

b. Merge the two clusters with the smallest distance or similarity.

• c. Update the clustering tree.
• d. As needed, determine the number or threshold of clusters and divide the clusters.
• In practical applications, hierarchical clustering is often used in image segmentation, text clustering, bioinformatics, social network analysis and other fields. For example, hierarchical clustering can be used to cluster a set of text documents into thematically related groups, or to segment a set of images into related regions. In bioinformatics, hierarchical clustering can be used to analyze gene expression data to determine which genes are related to each other, thereby identifying sets of genes associated with specific diseases.

In short, hierarchical clustering is a common unsupervised machine learning method, which can divide the data set into different clusters based on similarity and form a clustering hierarchy. Agglomerative hierarchical clustering and divisive hierarchical clustering are two common hierarchical clustering methods. In applications, hierarchical clustering can be used in image segmentation, text clustering, bioinformatics, social network analysis and other fields.

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