Unsupervised learning is a type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision. Here are some common examples of unsupervised learning:

1. Clustering

• • K-Means Clustering: It’s used to partition a dataset into k predefined distinct, non-overlapping subsets or clusters.
  • Hierarchical Clustering: This method creates a tree of clusters, allowing you to understand the relationships between the clusters.

1. Association

• • Apriori Algorithm: Often used in market basket analysis, this method identifies the frequent individual items in a database and extends them to larger and larger item sets as long as those item sets appear frequently enough in the database.
  • FP-Growth Algorithm: This method is an improvement over the Apriori algorithm, used to find frequent item sets in a transaction database without candidate generation.

1. Anomaly Detection

• • Isolation Forest: It’s used for detecting anomalies or outliers in the data. Anomalies are typically rare and different from normal data points.
  • One-Class SVM: This algorithm is used to detect outliers by training only on the ‘normal’ data and treating any deviation from this trained pattern as an anomaly.

1. Dimensionality Reduction

• • Principal Component Analysis (PCA): PCA is often used to reduce the dimensions of a dataset while preserving as much variability as possible.
  • t-Distributed Stochastic Neighbor Embedding (t-SNE): This technique is commonly used for visualizing high-dimensional data by reducing it to two or three dimensions.

1. Autoencoders

• • Vanilla Autoencoders: These neural networks aim to encode and decode the input data, learning to reconstruct the input data from a compressed representation.
  • Variational Autoencoders: These are used to generate new instances that are similar to the training data, commonly used in generative tasks like image generation.

1. Reinforcement Learning

• • Q-Learning: An off-policy algorithm for temporal difference learning, often used in training agents in environments where the current action does not depend on the policy being followed.

1. Natural Language Processing

• • Word2Vec: This method is used for converting words to vectors and discovering relationships between words without any labels.
  • Topic Modeling using Latent Dirichlet Allocation (LDA): This technique identifies topics within a set of documents, categorizing parts of texts without human intervention.

These examples cover a range of applications across various industries and research areas. Unsupervised learning offers valuable insights and is crucial for understanding underlying structures and relationships in unlabeled data.