What are the cluster analysis methods?

Cluster analysis is an unsupervised learning technique used to group data points with similar characteristics. Common cluster analysis methods include: K-Means, hierarchical clustering, mean shift clustering, Ward's method, DBSCAN, OPTICS, and spectral clustering.

Cluster analysis method

Cluster analysis is an unsupervised learning technique used to classify data points Group into clusters with similar characteristics. The following are commonly used cluster analysis methods:

1. K-Means

K-Means is a partition-based clustering algorithm that assigns data points into k clusters defined in advance. The algorithm iteratively assigns data points to the nearest cluster centers and then updates the cluster centers until the algorithm converges.

2. Hierarchical clustering

Hierarchical clustering builds a hierarchical cluster by gradually merging or splitting data points. It produces a diagram called a dendrogram that shows the hierarchical relationships of clusters.

3. Average shift clustering

Mean shift clustering is a distance-based clustering algorithm that calculates the distance between each data point and all other The distance between data points is used to determine clustering. It builds clusters by iteratively merging the closest data points.

4. Ward's method

Ward's method is a variance-based clustering algorithm that determines clusters by minimizing the variance of the data in the clusters. It builds clusters by iteratively merging data points with minimum variance.

5. DBSCAN

DBSCAN is a density-based clustering algorithm that identifies high-density regions in the data space as clusters. It determines clustering by specifying the minimum number (epsilon) and radius (minPts) of adjacent data points.

6. OPTICS

OPTICS is an extension of DBSCAN, which provides a hierarchical view of the clustering structure. It generates a reachability graph by calculating the reachability distance of each data point to all other data points.

7. Spectral clustering

Spectral clustering is an algorithm that uses graph theory technology for clustering. It works by representing the data as a graph and then using the eigenvectors of the graph to determine clusters.

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