What are the types of cluster analysis?

There are five main types of cluster analysis: hierarchical clustering (distance-based) partitioned clustering (k-means, k-medoid, fuzzy c-means) density clustering (DBSCAN, OPTICS) spectral clustering (Laplacian characteristic map) Other clustering algorithms (based on model, neural network)

Types of cluster analysis

Cluster analysis is an unsupervised machine learning technique used to group data points into categories with similar characteristics. There are many clustering algorithms, each with its own unique advantages and disadvantages.

Hierarchical clustering

• Distance-based: Use a distance metric such as Euclidean distance or cosine similarity to identify data points similarity between them.
• Agglomeration: Start with each data point and gradually merge the most similar clusters until the desired number of clusters is reached.
• Split: Start with a cluster containing all data points and gradually split it until the desired number of clusters is reached.

Divide clusters

• k-mean:Assign data points to k clusters, each cluster’s The center is the average of all data points in the cluster.
• k-center point: Assign data points to k clusters, and the center of each cluster is the centroid (median) of all data points in the cluster.
• Fuzzy c-means: Allows data points to belong to multiple clusters, whose membership is represented by a fuzzy value (0 to 1).

Density Clustering

• DBSCAN: Group data points into high-density areas, and the areas are separated by Separation of low-density areas.
• OPTICS: Organize data points into a hierarchy by calculating the density of each data point and its neighbors.

Spectral clustering

• Based on graph: Represent data points as nodes in the graph and use graph theory techniques to identify clusters.
• Laplacian eigenmap: Construct a Laplacian matrix using the similarity matrix of data points, and then perform eigendecomposition to identify clusters.

Other clustering algorithms

• Model-based: Use statistical models (such as Gaussian mixture models) to cluster data kind.
• Neural Network: Use a neural network architecture to learn the similarities between data points.

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