Clustering Methods: An Introduction to Unsupervised Learning Techniques
On Unsupervised Learning
Unsupervised learning is a machine learning technique where the algorithm learns patterns and relationships in the data without being explicitly trained on labeled examples. In unsupervised learning, the goal is to discover the underlying structure of the data, such as clusters, patterns, and relationships, without any prior knowledge of the labels or outcomes.
🦊 I also invite you to explore my posts on supervised and unsupervised learning, which can be found under the following topic lists: ‘Topics on Supervised Learning’, ‘Topics on Unsupervised Learning’, and ‘General Topics on Machine Learning’.
There are several methods of unsupervised learning, including clustering, dimensionality reduction, and anomaly detection.
Clustering is a technique for grouping similar data points into clusters based on their similarities and differences. The goal of clustering is to identify the natural groupings in the data without any prior knowledge of the labels or outcomes. The most popular types of clustering include hierarchical clustering, partitioning clustering, density-based clustering (DBSCAN), model-based clustering, and fuzzy clustering.
