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Unsupervised learning technique that separates similar data points into groups without tag.
Clustering (clustering) is a unsupervised learning technique that distinguishes unlabeled data points into groups (cuts) according to similarity among them. Unlike the classification, "correct answers" is not given to the clustering algorithm; the algorithm only discovers natural groups using the structure of the data itself, distance between data points or similarity criteria. The K-means algorithm divides the data from the pre-determined number of clusters, by assigning each point to the nearest cluster center; hierarchical clustering creates a tree structure by stepping data points; DBSCAN can detect different shapes and sizes clusters by working with intensity-based.
Clustering is used in many scenarios required to explore the previously unknown structure of data: like grouping customer segmentation, anomaly detection, gene expression images similar in genetic research and separation of large text or image collections into roughly categories. The quality of the cluster results is evaluated by metrics such as the silhouette score, but in the end, the number of "direct" clusters is a subjective decision, which should often be interpreted with the field information.
Clustering is used in many scenarios required to explore the previously unknown structure of data: like grouping customer segmentation, anomaly detection, gene expression images similar in genetic research and separation of large text or image collections into roughly categories. The quality of the cluster results is evaluated by metrics such as the silhouette score, but in the end, the number of "direct" clusters is a subjective decision, which should often be interpreted with the field information.