Here is an even more extensive and all-encompassing map of algorithms for extracting patterns from data, covering additional areas and providing more details: 1. Statistical Methods - Descriptive Statistics - Central Tendency (Mean, Median, Mode, Geometric Mean, Harmonic Mean, Trimmed Mean, Winsorized Mean) - Dispersion (Range, Variance, Standard Deviation, Coefficient of Variation, Quartiles, Interquartile Range, Mean Absolute Deviation, Median Absolute Deviation) - Skewness and Kurtosis (Pearson's Coefficient of Skewness, Bowley's Coefficient of Skewness, Moment Coefficient of Skewness, Percentile Coefficient of Skewness, Moors' Coefficient of Kurtosis, Moment Coefficient of Kurtosis, Percentile Coefficient of Kurtosis) - Correlation and Covariance (Pearson Correlation Coefficient, Spearman's Rank Correlation Coefficient, Kendall's Tau, Partial Correlation, Semipartial Correlation, Covariance Matrix) - Inferential Statistics - Hypothesis Testing (Z-test, t-test, F-test, Chi-Square Test, ANOVA, MANOVA, ANCOVA, Kruskal-Wallis Test, Friedman Test, McNemar's Test, Cochran's Q Test) - Confidence Intervals (Z-interval, t-interval, Proportion Interval, Variance Interval, Bonferroni Correction, Scheffe's Method, Tukey's Method, Dunnett's Method) - Non-parametric Tests (Mann-Whitney U, Wilcoxon Signed-Rank, Kruskal-Wallis, Friedman, Kolmogorov-Smirnov Test, Anderson-Darling Test, Shapiro-Wilk Test, Levene's Test, Bartlett's Test) - Bayesian Inference (Prior Distribution, Posterior Distribution, Bayes' Theorem, Conjugate Priors, Bayesian Credible Intervals, Bayesian Hypothesis Testing, Bayes Factors, Bayesian Model Selection) - Regression Analysis - Linear Regression (Simple, Multiple, Weighted) - Logistic Regression (Binary, Multinomial, Ordinal) - Polynomial Regression - Stepwise Regression (Forward Selection, Backward Elimination, Bidirectional Elimination) - Ridge Regression - Lasso Regression - Elastic Net Regression - Generalized Linear Models (GLMs) (Poisson Regression, Negative Binomial Regression, Gamma Regression, Tweedie Regression) - Nonlinear Regression (Exponential, Logarithmic, Power, Gompertz, Logistic) - Quantile Regression - Robust Regression (Least Absolute Deviations, Huber Regression, Bisquare Regression) - Generalized Additive Models (GAMs) - Segmented Regression - Hierarchical Linear Models (HLMs) / Multilevel Models - Mixed Effects Models - Structural Equation Modeling (SEM) - Bayesian Statistics - Bayesian Inference - Naive Bayes Classifier (Gaussian, Multinomial, Bernoulli, Complement) - Bayesian Networks (Directed Acyclic Graphs, Markov Blanket, d-Separation, Belief Propagation, Junction Tree Algorithm) - Markov Chain Monte Carlo (MCMC) Methods (Metropolis-Hastings Algorithm, Gibbs Sampling, Hamiltonian Monte Carlo, Reversible Jump MCMC, Parallel Tempering) - Variational Inference - Gaussian Processes - Dirichlet Processes - Chinese Restaurant Process - Indian Buffet Process - Hierarchical Dirichlet Process (HDP) - Latent Dirichlet Allocation (LDA) - Survival Analysis - Kaplan-Meier Estimator - Nelson-Aalen Estimator - Cox Proportional Hazards Model - Accelerated Failure Time (AFT) Model - Competing Risks Analysis - Frailty Models - Cure Models - Recurrent Event Analysis - Spatial Statistics - Spatial Autocorrelation (Moran's I, Geary's C, Getis-Ord G, Local Indicators of Spatial Association (LISA)) - Variograms and Semivariograms - Kriging (Ordinary, Universal, Indicator, Co-Kriging) - Spatial Regression (Spatial Lag Model, Spatial Error Model, Spatial Durbin Model, Geographically Weighted Regression) - Point Pattern Analysis (Complete Spatial Randomness, Intensity Estimation, K-Function, L-Function, Pair Correlation Function) - Spatial Interpolation (Inverse Distance Weighting, Thin Plate Splines, Radial Basis Functions) - Spatial Clustering (DBSCAN, OPTICS, Hierarchical Clustering, Spatial K-Means) - Causal Inference - Potential Outcomes Framework (Rubin Causal Model) - Propensity Score Methods (Matching, Stratification, Weighting, Regression Adjustment) - Instrumental Variables (Two-Stage Least Squares, Generalized Method of Moments) - Difference-in-Differences (DID) - Regression Discontinuity Design (RDD) - Synthetic Control Method - Mediation Analysis (Baron-Kenny Method, Sobel Test, Bootstrapping, Causal Mediation Analysis) - Directed Acyclic Graphs (DAGs) and Causal Diagrams - Experimental Design - Completely Randomized Design (CRD) - Randomized Complete Block Design (RCBD) - Latin Square Design - Factorial Designs (Full Factorial, Fractional Factorial) - Response Surface Methodology (RSM) (Central Composite Design, Box-Behnken Design) - Taguchi Methods (Orthogonal Arrays, Signal-to-Noise Ratio) - Split-Plot Designs - Repeated Measures Designs - Crossover Designs - Adaptive Designs - Meta-Analysis - Fixed Effects Model - Random Effects Model - Mixed Effects Model - Heterogeneity (Q-statistic, I-squared) - Publication Bias (Funnel Plot, Egger's Test, Trim and Fill Method) - Subgroup Analysis - Meta-Regression - Network Meta-Analysis - Bayesian Meta-Analysis 2. Machine Learning - Supervised Learning - Classification - Decision Trees & Random Forests (CART, ID3, C4.5, C5.0, Conditional Inference Trees, Extremely Randomized Trees) - Naive Bayes (Gaussian, Multinomial, Bernoulli, Complement) - Support Vector Machines (SVM) (Linear, Polynomial, Radial Basis Function (RBF), Sigmoid) - k-Nearest Neighbors (k-NN) (Brute Force, KD-Tree, Ball Tree) - Logistic Regression (Binary, Multinomial, Ordinal) - Neural Networks (Feedforward, Convolutional, Recurrent, Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Transformer) - Gradient Boosting Machines (GBM) (XGBoost, LightGBM, CatBoost, NGBoost) - AdaBoost (AdaBoost.M1, AdaBoost.M2, AdaBoost-SAMME, AdaBoost-SAMME.R) - Bayesian Classifiers (Naive Bayes, Gaussian Discriminant Analysis, Quadratic Discriminant Analysis, Bayesian Network Classifiers) - Discriminant Analysis (Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Regularized Discriminant Analysis (RDA)) - Rule-Based Classifiers (OneR, ZeroR, RIPPER, PART, CN2, M5Rules) - Stacking and Blending - Calibration (Platt Scaling, Isotonic Regression) - Ensemble Methods (Bagging, Boosting, Voting, Stacking, Blending, Cascading, Bayesian Model Combination) - Regression - Linear Regression (Ordinary Least Squares, Weighted Least Squares, Generalized Least Squares, Iteratively Reweighted Least Squares) - Polynomial Regression - Support Vector Regression (SVR) (Linear, Polynomial, Radial Basis Function (RBF), Sigmoid) - Decision Trees & Random Forests (CART, M5P, Conditional Inference Trees, Extremely Randomized Trees) - Neural Networks (Feedforward, Convolutional, Recurrent, Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Transformer) - Gradient Boosting Machines (GBM) (XGBoost, LightGBM, CatBoost, NGBoost) - AdaBoost.R2 - Gaussian Process Regression - Kriging (Ordinary, Universal, Indicator, Co-Kriging) - Kernel Ridge Regression - Bayesian Regression (Bayesian Linear Regression, Bayesian Ridge Regression, Bayesian Lasso) - Quantile Regression - Robust Regression (Least Absolute Deviations, Huber Regression, Bisquare Regression) - Isotonic Regression - Ensemble Methods (Bagging, Boosting, Stacking, Blending) - Semi-Supervised Learning - Self-Training - Co-Training - Tri-Training - Graph-Based Methods (Label Propagation, Label Spreading, Manifold Regularization) - Transductive SVM (TSVM) - Generative Models (Gaussian Mixture Models, Hidden Markov Models, Naive Bayes) - Expectation-Maximization (EM) Algorithm - Low-Density Separation - Entropy Minimization - Semi-Supervised Clustering (Constrained K-Means, Seeded K-Means, COP-KMeans, MPCK-Means) - Active Learning - Uncertainty Sampling (Least Confidence, Margin Sampling, Entropy Sampling) - Query-By-Committee (QBC) - Expected Model Change - Expected Error Reduction - Density-Weighted Methods - Batch-Mode Active Learning - Active Learning with Noisy Oracles - Active Learning for Structured Outputs - Active Transfer Learning - Reinforcement Learning - Q-Learning - SARSA (State-Action-Reward-State-Action) - Deep Q Networks (DQN) - Double DQN - Dueling DQN - Policy Gradients (REINFORCE, Actor-Critic, Advantage Actor-Critic (A2C), Asynchronous Advantage Actor-Critic (A3C)) - Proximal Policy Optimization (PPO) - Trust Region Policy Optimization (TRPO) - Monte Carlo Methods (First-Visit Monte Carlo, Every-Visit Monte Carlo) - Temporal Difference Learning (TD(0), TD(λ), SARSA(λ), Q(λ)) - Multi-Armed Bandits (ε-Greedy, Upper Confidence Bound (UCB), Thompson Sampling) - Model-Based Methods (Dyna, Prioritized Sweeping, Real-Time Dynamic Programming (RTDP)) - Inverse Reinforcement Learning - Hierarchical Reinforcement Learning - Exploration Strategies (Random, ε-Greedy, Softmax, Upper Confidence Bound (UCB), Thompson Sampling) - Transfer Learning in Reinforcement Learning - Unsupervised Learning - Clustering - Partitioning Methods - K-Means - K-Medoids (PAM, CLARA, CLARANS) - Fuzzy C-Means - Gaussian Mixture Models (GMM) - Expectation-Maximization (EM) Algorithm - Hierarchical Methods - Agglomerative Clustering (Single Linkage, Complete Linkage, Average Linkage, Ward's Method) - Divisive Clustering (DIANA, MONA) - Birch - CURE - ROCK - Density-Based Methods - DBSCAN - OPTICS - Mean Shift - DENCLUE - Kernel Density Estimation (KDE) - Grid-Based Methods - STING - CLIQUE - WaveCluster - OptiGrid - Model-Based Methods - Self-Organizing Maps (SOM) - Gaussian Mixture Models (GMM) - Hidden Markov Models (HMM) - Latent Dirichlet Allocation (LDA) - Subspace Clustering - CLIQUE - SUBCLU - FIRES - PROCLUS - ORCLUS - Consensus Clustering - Cluster Ensembles - Median Partition - Cluster-based Similarity Partitioning Algorithm (CSPA) - HyperGraph Partitioning Algorithm (HGPA) - Meta-CLustering Algorithm (MCLA) - Spectral Clustering - Unnormalized Spectral Clustering - Normalized Spectral Clustering (Shi-Malik, Ng-Jordan-Weiss) - Spectral Embedded Clustering - Kernel Spectral Clustering - Dimensionality Reduction - Feature Selection - Filter Methods (Variance Threshold, Correlation Coefficient, Chi-Square, Mutual Information, Information Gain, Fisher Score) - Wrapper Methods (Sequential Forward Selection, Sequential Backward Selection, Recursive Feature Elimination) - Embedded Methods (L1 Regularization (Lasso), Decision Tree Feature Importance, Gradient Boosting Feature Importance) - Feature Extraction - Principal Component Analysis (PCA) - Kernel PCA - Incremental PCA - Sparse PCA - t-Distributed Stochastic Neighbor Embedding (t-SNE) - Uniform Manifold Approximation and Projection (UMAP) - Isomap - Locally Linear Embedding (LLE) - Laplacian Eigenmaps - Hessian LLE - Modified LLE (MLLE) - Diffusion Maps - Autoencoder - Restricted Boltzmann Machine (RBM) - Independent Component Analysis (ICA) - Non-Negative Matrix Factorization (NMF) - Latent Dirichlet Allocation (LDA) - Latent Semantic Analysis (LSA) - Probabilistic Latent Semantic Analysis (PLSA) - Factor Analysis - Canonical Correlation Analysis (CCA) - Partial Least Squares (PLS) - Linear Discriminant Analysis (LDA) - Multidimensional Scaling (MDS) - Sammon Mapping - Stochastic Neighbor Embedding (SNE) - Gaussian Process Latent Variable Model (GPLVM) - Kernel Entropy Component Analysis (KECA) - Locality Preserving Projections (LPP) - Neighborhood Preserving Embedding (NPE) - Association Rule Mining - Apriori Algorithm - Eclat Algorithm - FP-Growth Algorithm - SETM Algorithm - AIS Algorithm - OPUS Search - Recursive Elimination - Frequent Pattern Tree (FP-Tree) - Closed Itemset Mining - A-Close Algorithm - CLOSET Algorithm - CHARM Algorithm - Maximal Itemset Mining - MaxMiner Algorithm -