algorithms

11 Data Mining Algorithms

1. Regression & Classification

  • Linear
  • Multivariate Linear
  • Logistic
  • Softmax
  • Vectorization
  • Gradient Calculation
  • Stochastic Gradient Descent (SGD)
  • Optimizers and Objectives

2. Regularization

  • Ridge regression

3. Clustering

  • k – Means
  • EM Algorithms

4. Unsupervised Learning

  • Autoencoders
  • PCA Whitening
  • sparse coding

5. Neural Network

  • Perceptrons
  • Backpropagation
  • Restricted Boltzmann Machines
  • Learning Vector Quantization

6. Deep Learning

  • Stacked Autoencoders
  • Convolution Neural Networks (Feature Extraction, Pooling)
  • Deep Boltzmann Machines
  • Deep Belief Networks

7. Decision Trees

  • ID3
  • C4.5
  • CART (Classification and regression tree)
  • Random Forests

8. Bayesian

  • Naïve Bayes
  • Gaussian Naïve Bayes
  • Bayesian Networks
  • Conditional Random Fields
  • Hidden Markov Models

9. Others

  • Support Vector Machines
  • Evolutionary Methods
  • Reinforcement Learning
  • Conditional Random Fields

10. Dimensionality Reduction

  • PCA

11. Ensemble Methods

  • Boosting
  • Bagging
  • Adaboost


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