It is valuable to mine the spatio-temporal patterns
from time series data set generated in real world applications.
However, two problems of data point granularity determining
and user behavior pattern describing are most challenging.
To address these problems, in this work, we propose a time
sequence based user behavior pattern describing method, in
which, the time-related events are mapped on the time stream
respectively. Then the user patterns are described according
to two aspects, namely the content-based and the structurebased patterns. Based on these two aspects, a new method- CS
Similarity, is proposed to measure the similarity of behavior
pattern between two independent users'. The experimental results
with the real transaction data of Guangzhou smart card show
that the proposed method (CSM) has a better performance on
fnding the similarity pattern among people in compare with the
classical methods of WM and SWM.