Hands-On Data Warehousing with Azure Data Factory

Identifying groups using clustering methods

Clustering methods are designed to find hidden patterns or groupings in a dataset. Unlike the supervised learning methods covered in previous sections (regression and classification tasks), these algorithms identify a grouping without any label to learn from. They do so through the selection of clusters based on similarities between elements.

This is an unsupervised learning technique that groups statistical units to minimize the intra-group distance and maximize the inter-group distance. The distance between the groups is quantified by means of similarity/dissimilarity measures defined between the statistical units. In the following graph, four clusters are identified in a specific data distribution: ...