Unsupervised machine learning: Difference between revisions

Clustering is an unsupervised learning technique used to group similar data points together. The objective of clustering is to discover natural groupings within the data. Clustering can be beneficial for tasks such as customer segmentation, anomaly detection and image segmentation.

Clustering algorithms range from k-means to hierarchical clustering and density-based clustering. K-means is one of the most popular algorithms, breaking data up into k clusters which represent similar data points. Hierarchical clustering creates a treelike structure of clusters with one root node representing all data points and leaf nodes representing individual points. Density-based clustering works by identifying high density regions within data.

Dimensionality reduction is an unsupervised learning technique used to reduce the number of features in data. The objective is to simplify the information while maintaining as much detail as possible. Dimensionality reduction can be beneficial for tasks such as data visualization, noise reduction, and feature extraction.

Dimensionality reduction algorithms range from principal component analysis (PCA), independent component analysis (ICA), and t-distributed stochastic neighbor embedding (t-SNE). PCA is a popular dimensionality reduction technique that works by projecting data onto a lower-dimensional space while retaining as much variance as possible. ICA separates data into independent components. Finally, t-SNE provides an effective means of visualizing high dimensional data sets.

- Natural language processing: Unsupervised learning is used to identify topics and themes from unstructured text data.

- Image and video analysis: Unsupervised learning can be applied to recognize objects, scenes, and events captured in images and videos.

- Anomaly Detection: Unsupervised learning can be employed to detect unusual patterns in data that could indicate anomalies or outliers.

- Fraud Detection: Unsupervised learning can be employed to detect fraudulent behavior by recognizing patterns inconsistent with normal patterns.

- Customer Segmentation: Unsupervised learning is utilized to group customers with similar characteristics together.