Clustering is a technique used in machine learning and data analysis to group similar items or observations together based on their features or attributes. It is an unsupervised learning technique, meaning that it does not require labeled data to learn patterns and groupings.

Figure 1. classification vs clustering. [1]

Figure 1 shows classification vs clustering. In clustering, the algorithm looks for similarities among the data points and groups them into clusters based on those similarities. The algorithm tries to maximize the similarity between the items in each cluster and minimize the similarity between items in different clusters.

Clustering has various applications, such as:

Image segmentation: Clustering can be used to segment images into different regions based on their color, texture, or other features.

Customer segmentation: Clustering can be used to group customers based on their buying behavior, demographics, or other attributes.

Anomaly detection: Clustering can be used to detect anomalies in data by identifying items that do not fit well into any of the clusters.

Document clustering: Clustering can be used to group similar documents together based on their content or topic.

There are different clustering algorithms, including K-means clustering, Hierarchical clustering, Density-based clustering, and more. The choice of algorithm depends on the type of data and the specific problem being solved.

References:

1. https://www.analyticsvidhya.com/blog/2021/05/what-why-and-how-of-spectral-clustering/

Cite this article:

Hana M (2023), Clustering, AnaTechmaz. pp.212