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What are Unsupervised Learning Techniques?
Unsupervised learning techniques are a subset of machine learning algorithms used to discover patterns, structures, or relationships in unlabeled data without explicit guidance or predefined outcomes. Unlike supervised learning, where labeled examples are provided for training, unsupervised learning aims to find inherent structures or patterns within the data itself.
Commonly used unsupervised learning techniques include:
Clustering: Clustering algorithms group similar data points together based on their intrinsic characteristics. They aim to identify clusters or subgroups within the data. Popular clustering algorithms include k-means, hierarchical clustering, and density-based clustering (e.g., DBSCAN).
Dimensionality Reduction: These techniques reduce the number of input variables while retaining the essential information. Dimensionality reduction methods, such as Principal Component Analysis (PCA) and t-SNE (t-distributed Stochastic Neighbor Embedding), transform high-dimensional data into a lower-dimensional space, simplifying the representation of the data.
Anomaly Detection: Anomaly detection algorithms identify unusual or abnormal data points that deviate significantly from the majority of the data. These techniques are useful for detecting outliers, fraud, or rare events. Examples include Gaussian Mixture Models (GMMs), Isolation Forest, and Local Outlier Factor (LOF).
Association Rule Mining: This technique discovers interesting relationships or associations between variables in the data. It identifies frequently occurring patterns or item sets in transactional data. The Apriori algorithm is a well-known approach for mining association rules.
Generative Models: Generative models learn the underlying probability distribution of the data and can generate new samples similar to the training data. Examples include Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), and Generative Adversarial Networks (GANs).
Unsupervised learning techniques have various applications, including customer segmentation, anomaly detection, recommender systems, data preprocessing, and exploratory data analysis. They enable insights and discoveries in large and complex datasets where the underlying patterns or structures are not explicitly known. However, the interpretation and evaluation of unsupervised learning results can be more challenging than in supervised learning, as there are no ground truth labels to compare against.
Unsupervised learning techniques are covered in more detail in module 5 of the CQF program.