Entropy can mean different things:
Computing
In computing, entropy is the
randomness collected by an operating
system or application for use in
cryptography or other uses that
require random data. This randomness
is often collected from hardware
sources, either pre-existing ones such
as mouse movements or specially
provided randomness generators.
Information theory
In information theory, entropy is a
measure of the uncertainty associated
with a random variable. The term by
itself in this context usually refers
to the Shannon entropy, which
quantifies, in the sense of an
expected value, the information
contained in a message, usually in
units such as bits. Equivalently, the
Shannon entropy is a measure of the
average information content one is
missing when one does not know the
value of the random variable
Entropy in data compression
Entropy in data compression may denote the randomness of the data that you are inputing to the compression algorithm. The more the entropy, the lesser the compression ratio. That means the more random the text is, the lesser you can compress it.
Shannon’s entropy represents an
absolute limit on the best possible
lossless compression of any
communication: treating messages to be
encoded as a sequence of independent
and identically-distributed random
variables, Shannon’s source coding
theorem shows that, in the limit, the
average length of the shortest
possible representation to encode the
messages in a given alphabet is their
entropy divided by the logarithm of
the number of symbols in the target
alphabet.