Classification Algorithms
Algorithms which solve the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.
Regression Algorithms
Algorithms which solve the problem of estimating the relationships among variables in a known data set.
Feature Selection Algorithm
Algorithms which solve the problem of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques are used for three reasons: simplification of models to make them easier to interpret by researchers/users, shorter training times,and enhanced generalization by reducing overfitting (reduction of variance)
Clustering Algorithm
Algorithms which solve the problem of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).
Association Rule & Frequent Itemset Mining Algorithm
Algorithms which solve the problem of identifying strong rules discovered in databases using some measure of "desirability" (or a weighted feature which a data set reflects strongly)
Manifold Learning Algorithm
Algorithms which learn an internal model of the data, which can be used to map points unavailable at training time into the embedding in a process often called out-of-sample extension.
Multi-Dimensional Scaling Algorithm
Algorithms which solve the problem of visualizing the level of similarity of individual cases of a data set.
It refers to a set of related ordination techniques used in information visualization, in particular to display the information contained in a distance matrix.
Nearest Neighbor Search Algorithm
Algorithms for finding closest (or most similar) points. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the function values.
Sequence Learning Algorithm
Algorithms which, given a training set, learns the most effective method to label a given data sequence to reduce task-specific error.
