Unsupervised Learning

Clustering is a type of unsupervised learning where the model groups similar data points together based on their features. It is used when you want to discover inherent structures in your data without predefined labels, such as customer segmentation, image compression, or anomaly detection.

Clustering is an unsupervised learning technique used to group data points into clusters based on similarity, without needing labeled data. It’s especially useful for data exploration, pattern discovery, or segmentation tasks where the underlying structure is unknown. Clustering algorithms aim to maximize intra-cluster similarity and minimize inter-cluster similarity. Different algorithms like K-Means, DBSCAN, and Hierarchical Clustering use different strategies to define and identify clusters. Choosing the right algorithm depends on the shape, density, and scale of the data. Evaluation is typically done using metrics like silhouette score or domain-specific validation.

Dimensionality reduction is a technique used to reduce the number of features in a dataset while preserving its essential characteristics. It is particularly useful for visualizing high-dimensional data, improving model performance, and reducing computational costs.

Dimensionality Reduction is an unsupervised learning approach used to reduce the number of input variables or features in a dataset while preserving as much relevant information as possible. It is commonly applied in situations where datasets have a high number of variables (high dimensionality), which can lead to problems like overfitting or the curse of dimensionality. Techniques like PCA (Principal Component Analysis) and t-SNE (t-distributed Stochastic Neighbor Embedding) help simplify data, improve model performance, and aid in visualization. PCA is linear and focuses on variance, while t-SNE is non-linear and emphasizes local structure. Dimensionality reduction is often a key preprocessing step before clustering or classification.

Association rule learning is a method used to discover interesting relationships between variables in large datasets. It is commonly used in market basket analysis, where the goal is to find sets of products that frequently co-occur in transactions.

Association Rule Learning is an unsupervised learning technique used to identify interesting relationships, patterns, or correlations among a set of items in large datasets. It is most widely known for its application in market basket analysis, where it helps discover which products are frequently bought together. This method generates rules in the form of IF-THEN statements based on support, confidence, and lift metrics. Two commonly used algorithms are Apriori, which generates candidate itemsets iteratively, and Eclat, which uses a more efficient vertical data format. While highly interpretable, association rule learning is computationally intensive on large datasets and requires careful threshold tuning to avoid a combinatorial explosion of rules.