🤖Statistical Prediction Unit 10 – Unsupervised Learning: PCA & Clustering
Unsupervised learning uncovers patterns in unlabeled data without predefined targets. It encompasses dimensionality reduction, which compresses high-dimensional data, and clustering, which groups similar data points. These techniques help manage complex datasets and reveal hidden structures.
Principal Component Analysis (PCA) is a key dimensionality reduction method, while K-means and hierarchical clustering are popular clustering algorithms. These approaches find applications in customer segmentation, anomaly detection, and image compression, offering valuable insights across various fields.
Unsupervised learning extracts patterns and insights from unlabeled data without predefined target variables
Dimensionality reduction techniques compress high-dimensional data into lower-dimensional representations while preserving important information
Helps mitigate the curse of dimensionality and improves computational efficiency
Clustering algorithms group similar data points together based on their inherent characteristics and patterns
Enables the discovery of hidden structures and relationships within the data
Principal Component Analysis (PCA) is a linear dimensionality reduction technique that identifies the directions of maximum variance in the data
K-means and hierarchical clustering are popular partitioning and hierarchical clustering algorithms, respectively
Evaluation metrics such as silhouette score and Davies-Bouldin index assess the quality and validity of clustering results
Real-world applications of unsupervised learning include customer segmentation, anomaly detection, and image compression
Dimensionality Reduction Techniques
Dimensionality reduction maps high-dimensional data to a lower-dimensional space while retaining the most important information
Reduces the number of features or variables in the dataset, making it more manageable and computationally efficient
Helps alleviate the curse of dimensionality, which refers to the challenges posed by high-dimensional data (sparsity, increased computational complexity)
Linear techniques, such as PCA, project the data onto a lower-dimensional linear subspace
Identifies the directions of maximum variance in the data and constructs new features (principal components) along those directions
Non-linear techniques, like t-SNE and UMAP, capture complex non-linear relationships in the data
Preserve the local structure and neighborhood relationships of the data points in the lower-dimensional space
Dimensionality reduction facilitates data visualization, noise reduction, and feature extraction
Principal Component Analysis (PCA)
PCA is a linear dimensionality reduction technique that transforms the data into a new coordinate system
Identifies the directions of maximum variance in the data, known as principal components
The first principal component captures the most variance, followed by the second, and so on
Projects the data onto the principal components to obtain a lower-dimensional representation
Preserves the global structure of the data while minimizing the reconstruction error
The number of principal components can be selected based on the desired level of variance retention (explained variance ratio)
PCA is sensitive to the scale of the features, so data standardization is often performed beforehand
Limitations of PCA include its linearity assumption and inability to capture non-linear relationships
Clustering Methods
Clustering is an unsupervised learning task that involves grouping similar data points together based on their inherent characteristics
Partitioning clustering algorithms, such as K-means, assign each data point to a single cluster
Aim to minimize the within-cluster variation and maximize the between-cluster separation
Hierarchical clustering algorithms, like agglomerative and divisive clustering, create a hierarchy of clusters
Agglomerative clustering starts with each data point as a separate cluster and iteratively merges the closest clusters
Divisive clustering starts with all data points in a single cluster and recursively splits the clusters
Density-based clustering, such as DBSCAN, identifies clusters as dense regions separated by areas of lower density
Model-based clustering, like Gaussian Mixture Models (GMM), assumes that the data is generated from a mixture of probability distributions
The choice of clustering algorithm depends on the nature of the data, desired cluster shape, and prior knowledge about the number of clusters
K-means Algorithm
K-means is a popular partitioning clustering algorithm that aims to partition the data into K clusters
Initializes K cluster centroids randomly or using techniques like K-means++
Iteratively assigns each data point to the nearest centroid based on a distance metric (Euclidean distance)
Updates the cluster centroids by computing the mean of the data points assigned to each cluster
Repeats the assignment and update steps until convergence (centroids no longer change significantly) or a maximum number of iterations is reached
The objective is to minimize the sum of squared distances between data points and their assigned centroids (within-cluster sum of squares)
The choice of K, the number of clusters, is a hyperparameter that needs to be specified in advance
Techniques like the elbow method or silhouette analysis can help determine an appropriate value for K
K-means is computationally efficient and scales well to large datasets but is sensitive to the initial centroid positions
Hierarchical Clustering
Hierarchical clustering creates a tree-like structure (dendrogram) that represents the hierarchical relationships between clusters
Agglomerative clustering is a bottom-up approach that starts with each data point as a separate cluster
Iteratively merges the closest clusters based on a linkage criterion (single, complete, average, or Ward's linkage)
Continues merging clusters until a desired number of clusters is reached or all data points belong to a single cluster
Divisive clustering is a top-down approach that starts with all data points in a single cluster
Recursively splits the clusters into smaller subsets based on a splitting criterion
Continues splitting until a desired number of clusters is reached or each data point forms its own cluster
The dendrogram visualizes the merging or splitting process and allows for the selection of the number of clusters by cutting the dendrogram at a specific height
Hierarchical clustering does not require specifying the number of clusters in advance but can be computationally expensive for large datasets
Evaluation Metrics
Evaluation metrics assess the quality and validity of clustering results in the absence of ground truth labels
Internal evaluation metrics measure the compactness and separation of clusters based on the data itself
Silhouette coefficient computes the average silhouette score for each data point, indicating how well it fits into its assigned cluster compared to other clusters
Davies-Bouldin index measures the ratio of within-cluster distances to between-cluster distances, with lower values indicating better clustering
External evaluation metrics compare the clustering results to external ground truth labels, if available
Adjusted Rand Index (ARI) measures the similarity between the clustering and the ground truth, accounting for chance agreements
Normalized Mutual Information (NMI) quantifies the mutual information between the clustering and the ground truth, normalized by the entropy of both partitions
Visualization techniques, such as scatter plots or t-SNE embeddings, can provide qualitative insights into the clustering results
The choice of evaluation metric depends on the specific goals and characteristics of the clustering task
Real-world Applications
Customer segmentation in marketing divides customers into distinct groups based on their behavior, preferences, or demographics
Enables targeted marketing strategies and personalized recommendations
Anomaly detection identifies unusual or abnormal instances in the data that deviate from the normal patterns
Detects fraudulent transactions, network intrusions, or manufacturing defects
Image compression reduces the size of images by identifying and preserving the most important visual information
PCA can be used to compress images by projecting them onto a lower-dimensional space
Document clustering organizes a large collection of text documents into coherent groups based on their content similarity
Facilitates information retrieval, topic modeling, and content recommendation systems
Bioinformatics applies clustering techniques to analyze gene expression data, identify co-expressed genes, and discover biological pathways
Social network analysis uses clustering to identify communities or groups of closely connected individuals within a social network
Recommender systems employ clustering to group users or items with similar preferences and generate personalized recommendations