Unsupervised Machine Learning

• Unsupervised machine learning is a type of machine learning where the algorithms learn patterns and relationships in data without being explicitly told what to look for.
• In unsupervised learning, the data used for training is not labeled, meaning that the algorithm must find patterns and structure in the data on its own.

Types of unsupervised machine learning

1. Dimensionality Reduction

Reduce the number of features or variables in a dataset while preserving the essential information in the data
Algorithm

• Principal Component Analysis (PCA)

2. Clustering

Group similar data points together based on some similarity metric.
Algorithms:

• K-means Clustering :
  To partition the data into a predetermined number of clusters, denoted by 'k' based on a specific distance metric
• Hierarchical clustering
  To build a hierarchy of clusters in a bottom-up or top-down manner
• Density Based Clustering ➛ DBSCAN
  To group data points based on the density of neighboring points.
• Gaussian Mixture Model (GMM)
• Fuzzy Clustering
• Spectral Clustering

3. Association rule mining

Identify relationships between variables in a dataset, such as products frequently purchased together in a retail setting.

4. Anomaly detection

Identifying rare or unusual data points that deviate significantly from the norm in a dataset.

5. Generative modeling

Learn the underlying probability distribution of the data and can be used to generate new samples that are similar to the original data