Many data streams consist of interleaved segments of a variety of other streams.
For example, a network link might carry some text, then some executable code, then some text again, then some scientific data, then some code again. However, the MMDC algorithm is good at dealing with interleaved streams. However, if compression performance drops significantly, it spawns off a new model and uses that. If the old model's performance improves, it switches back. Each model learns only at the times when it is performing well.