Classification
Visualize your data in a tree like format.
Multi-dimensional scaling (MDS) and Principal Component Analysis (PCA) are members of a larger family of techniques, called ordination methods, that include Principal Coordinate Analysis (PCoA), Seriation, Multidimensional Scaling and Factorial Analysis as well. In PCA (and in PCoA as well) the space is reduced to fewer dimensions, usually 2 or 3. In fact, PCA is just a rotation in a multi-dimensional space of the original system of axes. The original axes, representing the characters of the objects, are replaced by new orthogonal axes, which carry most of the variance. Two or three dimensional graphical representations can be proposed. PCA and PCoA are usually superior to agglomerative-clustering methods in the global representation or positioning of groups of objects. However, these ordination algorithms are less efficient in the representation of closely related objects.
Multi-dimensional scaling (MDS) is a set of related statistical techniques often used in information visualization for exploring similarities or dissimilarities in data. MDS is a special case of ordination. An MDS algorithm starts with a matrix of item–item similarities, it then assigns a location to each item in N-dimensional space, where N is specified a priori. For sufficiently small N, the resulting locations may be displayed in a graph or 3D visualization. (From http://en.wikipedia.org/wiki/Multidimensional_scaling)