Unsupervised Learning

In machine learning, unsupervised data refers to unlabeled data, i.e., data without predefined output categories or target values. Unlike supervised learning, which involves classification or regression, unsupervised learning models such as K-Means and DBSCAN are used to cluster data based on their distances and/or distributions. These models are particularly useful for tasks like anomaly detection, where the goal is to identify data points that exhibit unusual patterns or behaviors. In the remainder of this chapter, we review K-Means and DBSCAN as two well-known unsupervised learning algorithms. Notably, The main reference for the current topic is the scikit-learn [1].

• K-Means Clustering

• DBSCAN

References

[1] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.