Title: Clustering with Temporal Constraints on Spatio-Temporal Data of Human Mobility,
Abstract: Extracting significant places or places of interest (POIs) using individuals'
spatio-temporal data is of fundamental importance for human mobility analysis.
Classical clustering methods have been used in prior work for detecting POIs,
but without considering temporal constraints. Usually, the involved parameters
for clustering are difficult to determine, e.g., the optimal cluster number in
hierarchical clustering. Currently, researchers either choose heuristic values
or use spatial distance-based optimization to determine an appropriate
parameter set. We argue that existing research does not optimally address
temporal information and thus leaves much room for improvement. Considering
temporal constraints in human mobility, we introduce an effective clustering
approach - namely POI clustering with temporal constraints (PC-TC) - to extract
POIs from spatio-temporal data of human mobility. Following human mobility
nature in modern society, our approach aims to extract both global POIs (e.g.,
workplace or university) and local POIs (e.g., library, lab, and canteen).
Based on two publicly available datasets including 193 individuals, our
evaluation results show that PC-TC has much potential for next place prediction
in terms of granularity (i.e., the number of extracted POIs) and
predictability.