  Data compression algorithms are generally perceived as being of interest for
data communication and storage purposes only. However, their use in the field
of data classification and analysis is also of equal importance. Automatic data
classification and analysis finds use in varied fields like bioinformatics,
language and sequence recognition and authorship attribution. Different
complexity measures proposed in literature like Shannon entropy, Relative
entropy, Kolmogrov and Algorithmic complexity have drawbacks that make these
methods ineffective in analyzing short sequences that are typical in population
dynamics and other fields.
  In this paper, we study Non-Sequential Recursive Pair Substitution (NSRPS), a
lossless compression algorithm first proposed by Ebeling {\it et al.} [Math.
Biosc. 52, 1980] and Jim\'{e}nez-Monta\~{n}o {\it et al.}
[arXiv:cond-mat/0204134, 2002]). Using this algorithm, a new complexity measure
was recently proposed (Nagaraj {\it et al.} [arXiv:nlin.CD/1101.4341v1, 2011]).
In this work, we use NSRPS complexity measure for analyzing and classifying
symbolic sequences generated by 1D chaotic dynamical systems. Even with
learning data-sets of length as small as 25 and test data-sets of length as
small as 10, NSRPS measure is able to accurately classify the test sequence as
periodic, chaotic or random. For such short data lengths, methods which use
entropy measure and traditional lossless compression algorithm like LZ77
[A.Lempel and J.Ziv, IEEE Trans. Inform. Theory {\bf 22}, 75 (1976)] (used for
instance by {\it Gzip}, {\it Winzip} etc.) fails.
