Types of unsupervised learning

Unsupervised learning is a machine learning paradigm where the algorithm learns from unlabeled data to uncover patterns, relationships, and structures within the data. There are several types of unsupervised learning techniques:

Clustering: Clustering algorithms group similar data points together based on certain features or characteristics. Common clustering methods include:

K-Means Clustering: Divides data into ‘k’ clusters by minimizing the distance between data points and the cluster center.
Hierarchical Clustering: Builds a tree-like structure of clusters by iteratively merging or splitting clusters based on their similarities.
DBSCAN (Density-Based Spatial Clustering of Applications with Noise): Identifies clusters based on regions of high data point density.
Dimensionality Reduction: These techniques aim to reduce the number of features in the data while preserving its essential information. This is particularly useful for visualization and reducing the complexity of data. Common methods include:

Principal Component Analysis (PCA): Finds orthogonal components (principal components) that capture the maximum variance in the data.
t-Distributed Stochastic Neighbor Embedding (t-SNE): Emphasizes the preservation of pairwise distances between data points, making it effective for visualizing high-dimensional data.
Autoencoders: Neural networks that learn to encode and decode data, effectively learning a compact representation.
Anomaly Detection: Anomaly detection techniques identify data points that deviate significantly from the expected behavior or normal pattern. This is valuable for detecting rare and potentially critical events.

Isolation Forest: Constructs isolation trees to isolate anomalies with fewer splits.
One-Class SVM (Support Vector Machine): Trains a model to capture the boundary of normal data and identifies deviations.
Association Rule Learning: These techniques discover interesting relationships or patterns among variables in a dataset. Commonly used in market basket analysis and recommendation systems.

Apriori Algorithm: Finds frequent itemsets and generates association rules based on the frequency of co-occurrence.
Generative Adversarial Networks (GANs): GANs consist of a generator and a discriminator network that compete with each other. GANs can generate new data instances that resemble the training data distribution.

Latent Dirichlet Allocation (LDA): Used for topic modeling, LDA identifies topics within a collection of documents by modeling them as mixtures of underlying topics.

Self-Organizing Maps (SOM): A type of artificial neural network that produces a low-dimensional representation of input data while preserving topological relationships.

Clustering Validation Techniques: These are not algorithms themselves, but methods to evaluate the quality of clustering results. Examples include silhouette score, Davies–Bouldin index, and within-cluster sum of squares.

Unsupervised learning plays a crucial role in exploratory data analysis, feature engineering, and understanding data structure. It helps uncover hidden patterns and insights in large and complex datasets.

Unsupervised learning means there is no training phase where we feed labelled data to the learning algorithm in order to train the model. Instead the algorithm has to figure out things by itself.

Two types of unsupervised learning are Clustering and Association.

Clustering algorithms groups data into clusters based on similar patterns. An example, if you feed a large number of pictures of various animals, the clustering algorithm will group them into various clusters such as cats, dogs etc.

Association algorithms identity relationships between variables. A frequently quoted example is that if we feed sales data, it can identify patterns such as the people who bought item X has a probability of p% for buying item Y too.