  Clickstream analysis is getting more attention since the increase of usage in
e-commerce and applications. Beside customers' purchase behavior analysis,
there is also attempt to analyze the customer behavior in relation to the
quality of web or application design. In general, clickstream data can be
considered as a sequence of log events collected at different levels of web/app
usage. The analysis of clickstream data can be performed directly as sequence
analysis or by extracting features from sequences. In this work, we show how
representing and saving the sequences with their underlying graph structures
can induce a platform for customer behavior analysis. Our main idea is that
clickstream data containing sequences of actions of an application, are walks
of the corresponding finite state automaton (FSA) of that application. Our
hypothesis is that the customers of an application normally do not use all
possible walks through that FSA and the number of actual walks is much smaller
than total number of possible walks through the FSA. Sequences of such a walk
normally consist of a finite number of cycles on FSA graphs. Identifying and
matching these cycles in the classical sequence analysis is not straight
forward. We show that representing the sequences through their underlying graph
structures not only groups the sequences automatically but also provides a
compressed data representation of the original sequences.
