MinUS: Mining User Similarity with Trajectory Patterns
This tool, MinUS, integrates the technologies of trajectory pattern mining with
the state-of-the-art research on discovering user similarity with trajectory patterns. Specifically, with MinUS, we provide a platform to manage movement
datasets, and construct and compare users’ trajectory patterns. Tool users can
compare results given by a series of user similarity metrics, which allows them
to learn the importance and limitations of different similarity metrics and pro-
motes studies in related areas, e.g., location privacy. Additionally, MinUS can
also be used by researchers as a tool for preliminary process of movement data
and parameter tuning in trajectory pattern mining.
The architecture of MinUS is shown below.
MinUS has three function modules. The first module, data management, is in charge of managing movement datasets which are collected by different organisations.
Such a dataset consists of a number of users whose movement is stored in the form of daily trajectories.
This module keeps track of the statistic information about the users
in each dataset. The statistics will be updated automatically once the values of the fields are available.
The second function module, mobility mining, takes users' daily
trajectories as input and outputs their trajectory patterns.
At the first step, we traverse all of the selected users' trajectories and
detect their stay points, the centroids of small areas where a user stayed
for a certain amount of time.
A place of interest (PoI) is an area where users
frequently visit, stay for a while and preform certain activities,
such as supermarkets and theatres.
Based on this interpretation, at the second step, we calculate users' PoIs by
identifying the regions where stay points are densely located.
We implement a hierarchical clustering algorithm to calculate the clusters of
stay points which are close to each other.
Outlying stay points that are isolated from other points may increase the size
of some PoIs. We make use of LOF (local outlier factor) to measure the
extent of isolation of a stay point and remove a certain percentage
of stay points that are most isolated.
With extracted PoIs, users' trajectories are transformed into sequences of
PoIs. At the last step, we explore the trajectory pattern mining tool
Giannotti et al. to extract trajectory patterns.
With a visualisation interface called
MapView, tool users can put all the intermediate results on the map.
We show the screen shot of the visualisation interface.
The third module, similarity calculation, calculates the similarity
values between users selected by tool users using the chosen similarity metric.
The tool users start with selecting a
subset of users and the tool will return the similarity values between
any two selected users. The results are visualised by a grid where
the grey level of each cell indicates the similarity values between
a pair of users. So far, we have implemented three categories of user
similarity metrics: maximal trajectory pattern based
(check this paper
and this paper
common pattern set based
(check this paper
and Hausdorff distance based
(check this thesis
MinUS also allows for taking into account location semantics
and temporal semantics in the calculation.
Location semantics denotes the functionalities of a PoI, e.g., restaurant and
school, while temporal semantics represents the information revealed by
time, e.g., weekends and weekdays.
- You can download the compiled version of the program here.
- The source code of the program is available here.
- A user manual is available as standalone download here.
- An example dataset can be downloaded here.
The tool requires JDK 7 or later.
Example datasets containing GPS trajectories can be obtained from the following links:
- Geolife (A dataset from Microsoft Research)
- Yonsei (A mobility data collected by LifeMap monitoring system at Yonsei University in Seoul)