Use unlabeled data, modeling the underlying structure or distribution.
#Main Algorithms
- K-Means Clustering
- Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
- Gaussian Mixture Models (GMM)
- Hierarchical Clustering
- Principal Component Analysis 1 (PCA)
- t-SNE
- Spectral Clustering
- Isomap
- AutoEncoder
#Examples
#Clustering
Grouping similar data points based on intrinsic characteristics. e.g. identifying natural groupings or sub-populations in data; highlighting patterns or anomalies (e.g. segmentation in marketing)
#Dimensionality Reduction
Reducing the number of features or dimensions in a dataset while retaining as much relevant information as possible. e.g. noise reduction, visualization, computational complexity.
#Outlier Detection
Identifying data points that deviate significantly from the majority of the dataset. These outliers may represent rare or anomalous events, errors, or unusual patterns.
Undersampling