Introduction

In unsupervised learning, descriptive models are used for exploratory analysis to uncover patterns in unlabeled data. Examples of unsupervised learning tasks include algorithms for clustering and those for dimension reduction. In clustering, observations are assigned to groups in which there is high within-group homogeneity and between-group heterogeneity. Simply put, observations are placed into clusters of samples with other observations that are very similar. Use cases for clustering algorithms are vast. For example, analysts seeking to elevate sales by targeting selected customers for marketing advertisements and promotions separate customers by their shopping behavior.

Note

Additionally, hierarchical clustering has been implemented in academic neuroscience and motor behavior research (https://www.researchgate.net/profile/Ming-Yang_Cheng/project/The-Effect-of-SMR-Neurofeedback-Training-on-Mental-Representation-and-Golf-Putting-Performance/attachment/57c8419808aeef0362ac36a5...