Naïve Bayes Fast Low Scales over vary large datasets. Speed at the expense of accuracy Neural Network Slow Low Centering and Scaling, Remove Highly Correlated Predictors Fast Low Scales over medium size K Nearest Neighbors datasets Ridge Fast High Centering and Scaling Regression Nearest Fast Medium Shrunken Centroids MARS Fast High Slow Low Accurate Bagged / Boosted Trees Unsupervised learning problems do not have labeled data and can be classified into two subgroups: clustering algorithms and dimensionality reduction techniques. Clustering algorithms attempt to find underlying structure in the data by determining groups of similar data. Dimensionality reduction algorithms are typically used for preprocessing of datasets prior to the application of other algorithms. lists common clustering Table 10 algorithms, and lists common dimensionality reduction techniques. Table 11 Table 10. Unsupervised Clustering Algorithms Name Pre-Processing Interpretability Notes Missing value sensitivity, Medium Scales over large datasets for K -means Centering and Scaling clustering tasks, must specify number of clusters (k) Must specify number of Fuzzy c-means clusters (k) Specify k for probability tasks Must specify number of Gaussian clusters (k) Must specify number of Hierarchical clusters (k) DBSCAN Do not have to specify number of clusters (k) While technically dimension reduction may be a preprocessing technique, which transforms predictors, usually driven for computational reasons, some consider dimensionality reduction (or data reduction) techniques a class of unsupervised algorithms because they are also a solution for unlabeled data.
Naïve Bayes Fast Low Scales over vary large datasets. Speed at the expense of accuracy Neural Network Slow Low Centering and Scaling, Remove Highly Correlated Predictors K Nearest Neighbors Fast Low Scales over medium size datasets Ridge Regression Fast High Centering and Scaling Nearest Shrunken Centroids Fast Medium MARS Fast High Bagged / Boosted Trees Slow Low Accurate Unsupervised learning problems do not have labeled data and can be classified into two subgroups: clustering algorithms and dimensionality reduction techniques. Clustering algorithms attempt to find underlying structure in the data by determining groups of similar data. Dimensionality reduction algorithms are typically used for preprocessing of datasets prior to the application of other algorithms. Table 10 lists common clustering algorithms, and Table 11 lists common dimensionality reduction techniques. Table 10. Unsupervised Clustering Algorithms Name Pre-Processing Interpretability Notes K -means Missing value sensitivity, Centering and Scaling Medium Scales over large datasets for clustering tasks, must specify number of clusters (k) Fuzzy c-means Must specify number of clusters (k) Gaussian Specify k for probability tasks Must specify number of clusters (k) Hierarchical Must specify number of clusters (k) DBSCAN Do not have to specify number of clusters (k) While technically dimension reduction may be a preprocessing technique, which transforms predictors, usually driven for computational reasons, some consider dimensionality reduction (or data reduction) techniques a class of unsupervised algorithms because they are also a solution for unlabeled data.