  Discovering human mobility patterns with geo-location data collected from
smartphone users has been a hot research topic in recent years. In this paper,
we attempt to discover daily mobile patterns based on GPS data. We view this
problem from a probabilistic perspective in order to explore more information
from the original GPS data compared to other conventional methods. A
non-parameter Bayesian modeling method, Infinite Gaussian Mixture Model, is
used to estimate the probability density for the daily mobility. Then, we use
Kullback-Leibler divergence as the metrics to measure the similarity of
different probability distributions. And combining Infinite Gaussian Mixture
Model and Kullback-Leibler divergence, we derived an automatic clustering
algorithm to discover mobility patterns for each individual user without
setting the number of clusters in advance. In the experiments, the
effectiveness of our method is validated on the real user data collected from
different users. The results show that the IGMM-based algorithm outperforms the
GMM-based algorithm. We also test our methods on the dataset with different
lengths to discover the minimum data length for discovering mobility patterns.
