Some Data Mining Methods

Antoine Naud
February 17,2004

This list presents a categorization of various data mining methods into categories corresponding to a type of application. It is simplified and not complete. There is an overlap between the three types of applications (CLAS, DRED, CLUS) in that sense that many methods can be found in more than one category. For instance, most neural networks are designed to solve classification tasks, but they are also used for clustering or dimensionality reduction tasks.
Supervised learning means that information about which sample or data vector belongs to which class is known and used during the teaching of the model.
Unsupervised learning means that class information (whether we know it or not) is not used during the learning process. Most of classifiers need class information so they are supervised methods. Clustering methods are often used when we don't know anything about the classes, and we want the model itself to discover the classes by grouping/partitioning the samples.

1) CLAS - Main families of classification methods:

2) DRED - Dimensionality reduction makes high-dimensional data problems more tractable.
a) feature extraction:
b) feature selection:

3) CLUS - Clustering, partitioning: