Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Clustering Users by Their Mobility Behavioral Patterns

Clustering Users by Their Mobility Behavioral Patterns 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 mobility movement 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Knowledge Discovery from Data (TKDD) Association for Computing Machinery

Loading next page...
 
/lp/association-for-computing-machinery/clustering-users-by-their-mobility-behavioral-patterns-Tube0KglyF

References (63)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2019 ACM
ISSN
1556-4681
eISSN
1556-472X
DOI
10.1145/3322126
Publisher site
See Article on Publisher Site

Abstract

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 mobility movement 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.

Journal

ACM Transactions on Knowledge Discovery from Data (TKDD)Association for Computing Machinery

Published: Aug 20, 2019

Keywords: Clustering trajectories

There are no references for this article.