In hierarchical clustering, the data set is split into classes and within them, into subclasses and so on. There are pros and cons on the use of different methods. Nevertheless, apart from considerations on their relative efficiencies, it is interesting to highlight one interesting property of divisive methods in the analysis of huge amounts of data. Often it is more interesting to have only the most important parts of the hierarchy. Divisive methods can be stopped when a given level of variability has been reached for all the clusters.
Neural networks are more efficient than algebraic methods in dealing with big amounts of noisy data.
In fact, Kohonen SOM have been successfully used to analyse DNA-array data. Indifferent It does not provide new advantages when applied to DNA-array data. This variability depends on the measure you are using. You can also choose a variability level baser on the distribution of random values. See in the figure an example in which a dataset of 800 genes has been reduced to only 13 clusters, for which the corresponding average profile has been plotted.