Dynamic Frequent Trajectory Mining Algorithm Based on Confidence of
Suffix Subsequence
Abstract
With the popularization of smart devices and the continuous development
of wireless communication technology, a large amount of trajectory data
is recorded in wireless networks, and frequent item mining for
trajectory data has become a research hotspot. In order to rapid update
frequent trajectory set when new data is added to the original
trajectory database, and to solve the problem that trajectory database
occupies a large amount of storage space, we propose a new dynamic
PrefixSpan algorithm based on the confidence of suffix subsequence
(CSS-PrefixSpan). This algorithm makes full use of the information of
the frequent trajectory set and estimates the support of infrequent
trajectory by using the confidence of infrequent trajectory suffix
subsequence. When incremental trajectory data is added to the original
trajectory database, CSS-PrefixSpan can dynamically update frequent
trajectory set and no longer need to store the original trajectory data
after mining frequent trajectories. Through two trajectory dataset
mining experiments, the accuracy of the support estimation and the
effectiveness of proposed algorithm are verified.