Unsupervised learning helps detect shady people

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. In unsupervised learning, the goal is to infer the natural structure present within a set of data points. This can involve tasks such as clustering, where the algorithm identifies groups of similar data points, or dimensionality reduction, which aims to simplify the input data while retaining its essential features. These techniques are particularly useful when working with large and complex datasets, as they can reveal patterns and relationships that may not be immediately apparent to human observers.

Some examples:

Clustering: Grouping data points into clusters where points in the same cluster are more similar to each other than to points in other clusters.

• K-Means: A popular clustering algorithm that partitions data into K clusters.
• DBSCAN: Density-Based Spatial Clustering of Applications with Noise, which finds clusters based on the density of data points.

Dimensionality Reduction: Reducing the number of random variables under consideration, often used for data visualization and noise reduction.

• Principal Component Analysis (PCA): A method that transforms the data into a set of orthogonal components ordered by the amount of variance they explain.
• t-Distributed Stochastic Neighbor Embedding (t-SNE): A technique that reduces dimensions while preserving the local structure of the data for visualization.

Anomaly Detection: Identifying unusual data points that do not fit the general pattern of the data.

• Isolation Forest: An algorithm that isolates observations by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature.

Examples:

1. Market Basket Analysis: Finding associations between products in transaction data. For example, discovering that customers who buy bread are also likely to buy butter.
2. Customer Segmentation: Grouping customers into segments based on purchasing behavior, demographics, etc., to tailor marketing strategies.
3. Image Compression: Reducing the size of image files by identifying and removing redundant or less important information.