  Rapid growth of genetic databases means huge savings from improvements in
their data compression, what requires better inexpensive statistical models.
This article proposes automatized optimizations e.g. of Markov-like models,
especially context binning and model clustering. While it is popular to just
remove low bits of the context, proposed context binning automatically
optimizes such reduction as tabled: state=bin[context] determining probability
distribution, this way extracting nearly all useful information also from very
large contexts, into a relatively small number of states. The second proposed
approach: model clustering uses k-means clustering in space of general
statistical models, allowing to optimize a few models (as cluster centroids) to
be chosen e.g. separately for each read. There are also briefly discussed some
adaptivity techniques to include data non-stationarity.
