Clustering Users by Their Mobility Behavioral Patterns

Irad Ben-Gal, Shahar Weinstock, Gonen Singer, Nicholas Bambos

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


The immense stream of data from mobile devices during recent years enables one to learn more about human behavior and provide mobile phone users with personalized services. In this work, we identify clusters of users who share similar mobility behavioral patterns. We analyze trajectories of semantic locations to find users who have similar mobility "lifestyle," even when they live in different areas. For this task, we propose a new grouping scheme that is called Lifestyle-Based Clustering (LBC).We represent the mobilitymovement of each user by a Markov model and calculate the Jensen Shannon distances among pairs of users. The pairwise distances are represented by a similarity matrix, which is used for the clustering. To validate the unsupervised clustering task, we develop an entropy-based clustering measure, namely, an index that measures the homogeneity of mobility patterns within clusters of users. The analysis is validated on a real-world dataset that contains location-movements of 50,000 cellular phone users that were analyzed over a two-month period.

Original languageEnglish
Article number45
JournalACM Transactions on Knowledge Discovery from Data
Issue number4
StatePublished - Aug 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019 Association for Computing Machinery. All rights reserved.


  • Clustering trajectories
  • clustering evaluation


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