Clustering of check-in sequences using the mixture Markov chain process
Social and Information Networks
2021-06-24 v1 Computers and Society
Machine Learning
Abstract
This work is devoted to the clustering of check-in sequences from a geosocial network. We used the mixture Markov chain process as a mathematical model for time-dependent types of data. For clustering, we adjusted the Expectation-Maximization (EM) algorithm. As a result, we obtained highly detailed communities (clusters) of users of the now defunct geosocial network, Weeplaces.
Keywords
Cite
@article{arxiv.2106.12039,
title = {Clustering of check-in sequences using the mixture Markov chain process},
author = {Elena Shmileva and Viktor Sarzhan},
journal= {arXiv preprint arXiv:2106.12039},
year = {2021}
}
Comments
16 pages, 4 figures