  Recent improvements in positioning technology has led to a much wider
availability of massive moving object data. A crucial task is to find the
moving objects that travel together. Usually, these object sets are called
spatio-temporal patterns. Due to the emergence of many different kinds of
spatio-temporal patterns in recent years, different approaches have been
proposed to extract them. However, each approach only focuses on mining a
specific kind of pattern. In addition to being a painstaking task due to the
large number of algorithms used to mine and manage patterns, it is also time
consuming. Moreover, we have to execute these algorithms again whenever new
data are added to the existing database. To address these issues, we first
redefine spatio-temporal patterns in the itemset context. Secondly, we propose
a unifying approach, named GeT_Move, which uses a frequent closed itemset-based
spatio-temporal pattern-mining algorithm to mine and manage different
spatio-temporal patterns. GeT_Move is implemented in two versions which are
GeT_Move and Incremental GeT_Move. To optimize the efficiency and to free the
parameters setting, we also propose a Parameter Free Incremental GeT_Move
algorithm. Comprehensive experiments are performed on real datasets as well as
large synthetic datasets to demonstrate the effectiveness and efficiency of our
approaches.
