clustering

Clustering algorithms take data and tries to sort it into clusters each with some form of similar characteristics. This is like you might do if looking a scatter graph and noticing that the data falls into two or three groups. Examples of cluster analysis includes k-means which is based around calculating centroids of each cluster and Kohonen networks (or self-organsing maps) which are a form of neural netwrok where similar data points are classified close to one another on a two dimensional grid. Clustering algorithms are a form of unsupervised learning, but there are also semi-supervised forms where some or all of the training set has a classification and the clustering algorithm tries to ensure that each cluster has a consistent label, even if there may be several clusters for each label.