Sidst opdateret 6. oktober 2023
What is a clustering method?
Clustering methods are techniques used to group similar data points or objects together into clusters or groups based on their inherent similarities or patterns in the data. The primary goal of clustering is to discover hidden structures in the data and organize it in a way that makes it easier to understand and interpret.
Types of clustering methods
- Hierarchical clustering: This method creates a tree-like structure of clusters with each level of the hierarchy representing a different level of granularity in the clustering. You can cut the tree at a certain level to obtain the desired number of clusters.
- K-Means clustering: K-Means is one of the most popular clustering algorithms. It partitions data into K clusters, where K is a user-defined parameter. It assigns each data point to the cluster with the nearest centroid.
- Mean shift: This clustering algorithm is used for mode-seeking and tends to find clusters with varying densities.
- Gaussian Mixture Models (GMM): GGM represents data as a mixture of Gaussian distributions, making it suitable for data with complex distribution patterns.
Clustering is an unsupervised learning task that groups similar data points together based on inherent similarities, without prior knowledge of class labels. Classification, on the other hand, is a supervised learning task that assigns predefined class labels to data points based on their features.
The choice of clustering algorithm depends on your data’s characteristics and the goals of your analysis. Some algorithms work better for certain types of data and cluster shapes.
The Silhouette Value is a metric used to evaluate the quality of clusters produced by a clustering algorithm. It measures how similar an object is to its own cluster compared to our clusters. A higher Silhouette value indicated better-defined clusters.