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Unsupervised learning works with untagged data and its purpose is to create clusters based on the characteristics of the data. Unlike supervised learning, untagged data is used instead of labeled data. After the data are divided into groups according to their similarity or distance, labeling is done with the help of an expert. Two applications that stand out in unsupervised learning are clustering and association rule mining. Clustering is the assignment of data points to groups called clusters. It has two types: partitioned and hierarchical methods. In partitioned clustering, a data point can only be in one cluster. In hierarchical clustering, a point can be hierarchically located in more than one cluster. In association rules mining, association rules focused on finding rules based on relationships between events are used in mining relationships between attributes.