MESO is based on the leader follower algorithm (Figure ? algorithm). 

Along with the above information, a simple two (2) step procedure can be used to aid a user in choosing a PLS best for the dataset to be reduced. First, the user needs to extrapulate certain information about the data. For instance, the size of the dataset, its feature dimensions, whether it is supervised or unsupervised, an estimate of how noisy the data is, in other words how much data is missing, and finally, what domain is the dataset collected from? Next, based on the previous information, the user can now determine the desired characteristics of the prototype learning scheme for generating prototypes. To make clearer the use of the two step procedure stated here, the following sections details three (3) general cases as examples.

\subsection{Three (3) Example Cases Defined}

Assuming that the size of a dataset is classified as huge, large, medium and small, where huge > 100000, large is >= 10000 but less than 100000, medium 5000 to 9999, and small is <5000, if the size of your data set is relatively small to medium almost all PLS can accommodate it. On the other hand, faced with a large data set some schemes collapse in terms of accuracy. So yes your dataset size matters if your dataset is large. Another reason size would matter is speed. How fast do you want results? Some PLS especially those which replace training patterns tend to be slower than those which select prototypes because their methodology requires the use of all the patterns in a training set. Therefore the speed would depend on the size of your data set. The following section explores how some PLS deals with the issue of large datasets.
